Systems and methods relating to asynchronous resolution of customer requests in contact center

ABSTRACT

A method for resolving customer requests that includes: providing a personal bot assistant and an asynchronous resolution facilitator; receiving a customer request from a first customer and producing a transcript thereof; determining an intent based on the transcript and customer information relating to the determined intent; transmitting an initial set of data to the asynchronous resolution facilitator and assembling therefrom a resolution package that includes an agent interface showing information required to expeditiously resolve the customer request including one or more recommended business processes; displaying the agent interface on a screen of the agent device; receiving input from the agent device that indicates the agent has completed preparing a resolution for the customer request; and providing notification to the first customer of the achieved resolution via the personal device of the first customer.

CROSS REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional PatentApplication No. 63/069,934, titled “SYSTEMS AND METHODS RELATING TOASYNCHRONOUS RESOLUTION OF CUSTOMER REQUESTS IN CONTACT CENTER”, filedin the U.S. Patent and Trademark Office on Aug. 25, 2020, the contentsof which are incorporated herein.

BACKGROUND

The present invention generally relates to telecommunications systems inthe field of customer relations management including customer assistancevia call or contact centers and internet-based service options. Moreparticularly, but not by way of limitation, the present inventionpertains to systems and methods for automating aspects of contact centeroperations and customer experience, including customer services offeredthrough an application executed on a mobile computing device.

BRIEF DESCRIPTION OF THE INVENTION

The present invention includes a computer-implemented method forresolving customer requests that includes: providing a personal botassistant and an asynchronous resolution facilitator; receiving acustomer request from a first customer, the first customer request beingreceived in a first conversation between the first customer and thepersonal assistant bot via a personal device corresponding to the firstcustomer; producing a transcript of the first conversation; determiningan intent of the customer request based on the transcript; determiningcustomer information relating to the first customer relevant to thedetermined intent; transmitting an initial set of data to theasynchronous resolution facilitator, the initial set of data includingthe transcript of the first conversation, the determined intent of thecustomer request, and the customer information; receiving the initialset of data and assembling a resolution package that includesinstructions for displaying an agent interface and metadata associatedwith the agent interface, wherein the assembling the resolution packagecomprises: determining, based on the intent of the customer request, oneor more recommended business processes for resolving the customerrequest; generating the agent interface such that the agent interface,once displayed, visually communicates at least a portion of the initialset of data and the one or more recommended business processes;determining the metadata for associating with the agent interface,wherein the metadata comprises criteria for routing the customer requestbased on the determined intent; transmitting the resolution package to arouting engine of the contact center; using the routing engine to routethe resolution package to an agent device of a selected agent of thecontact center, the selected agent being selected from among the agentsof the contact center based on the criteria of the metadata; and basedon the instructions received in the resolution package, displaying theagent interface on a screen of the agent device; subsequent todisplaying the agent interface on the agent device, receiving input fromthe agent device that indicates the selected agent deems a resolution ofthe customer request is achieved; and providing, by the personal botassistant, notification to the first customer of the achieved resolutionvia the personal device of the first customer.

These and other features of the present application will become moreapparent upon review of the following detailed description of theexample embodiments when taken in conjunction with the drawings and theappended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete appreciation of the present invention will become morereadily apparent as the invention becomes better understood by referenceto the following detailed description when considered in conjunctionwith the accompanying drawings, in which like reference symbols indicatelike components, wherein:

FIG. 1 depicts a schematic block diagram of a computing device inaccordance with exemplary embodiments of the present invention and/orwith which exemplary embodiments of the present invention may be enabledor practiced;

FIG. 2 depicts a schematic block diagram of a communicationsinfrastructure or contact center in accordance with exemplaryembodiments of the present invention and/or with which exemplaryembodiments of the present invention may be enabled or practiced;

FIG. 3 is schematic block diagram showing further details of a chatserver operating as part of the chat system according to embodiments ofthe present invention;

FIG. 4 is a schematic block diagram of a chat module according toembodiments of the present invention;

FIG. 5 is an exemplary customer chat interface according to embodimentsof the present invention;

FIG. 6 is a block diagram of a customer automation system according toembodiments of the present invention;

FIG. 7 is a flowchart of a method for automating an interaction onbehalf of a customer according to embodiments of the present invention;

FIG. 8 is a schematic representation of an exemplary system including apersonal bot and personalized customer profile in accordance with thepresent invention;

FIG. 9 is a method for creating a personalized customer profile inaccordance with the present invention; and

FIG. 10 is a method for asynchronously resolving customer requests inaccordance with embodiments of the present invention.

DETAILED DESCRIPTION

For the purposes of promoting an understanding of the principles of theinvention, reference will now be made to the exemplary embodimentsillustrated in the drawings and specific language will be used todescribe the same. It will be apparent, however, to one having ordinaryskill in the art that the detailed material provided in the examples maynot be needed to practice the present invention. In other instances,well-known materials or methods have not been described in detail inorder to avoid obscuring the present invention. Additionally, furthermodification in the provided examples or application of the principlesof the invention, as presented herein, are contemplated as wouldnormally occur to those skilled in the art.

As used herein, language designating nonlimiting examples andillustrations includes “e.g.”, “i.e.”, “for example”, “for instance” andthe like. Further, reference throughout this specification to “anembodiment”, “one embodiment”, “present embodiments”, “exemplaryembodiments”, “certain embodiments” and the like means that a particularfeature, structure or characteristic described in connection with thegiven example may be included in at least one embodiment of the presentinvention. Thus, appearances of the phrases “an embodiment”, “oneembodiment”, “present embodiments”, “exemplary embodiments”, “certainembodiments” and the like are not necessarily referring to the sameembodiment or example. Further, particular features, structures orcharacteristics may be combined in any suitable combinations and/orsub-combinations in one or more embodiments or examples.

Those skilled in the art will recognize from the present disclosure thatthe various embodiments may be computer implemented using many differenttypes of data processing equipment, with embodiments being implementedas an apparatus, method, or computer program product. Exampleembodiments, thus, may take the form of an entirely hardware embodiment,an entirely software embodiment, or an embodiment combining software andhardware aspects. Example embodiments further may take the form of acomputer program product embodied by computer-usable program code in anytangible medium of expression. In each case, the example embodiment maybe generally referred to as a “module”, “system”, or “method”.

The flowcharts and block diagrams provided in the figures illustratearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products in accordance withexample embodiments of the present invention. In this regard, it will beunderstood that each block of the flowcharts and/or block diagrams—orcombinations of those blocks—may represent a module, segment, or portionof program code having one or more executable instructions forimplementing the specified logical functions. It will similarly beunderstood that each of block of the flowcharts and/or block diagrams—orcombinations of those blocks—may be implemented by special purposehardware-based systems or combinations of special purpose hardware andcomputer instructions performing the specified acts or functions. Suchcomputer program instructions also may be stored in a computer-readablemedium that can direct a computer or other programmable data processingapparatus to function in a particular manner, such that the programinstructions in the computer-readable medium produces an article ofmanufacture that includes instructions by which the functions or actsspecified in each block of the flowcharts and/or block diagrams—orcombinations of those blocks—are implemented.

Computing Device

It will be appreciated that the systems and methods of the presentinvention may be computer implemented using many different forms of dataprocessing equipment, for example, digital microprocessors andassociated memory, executing appropriate software programs. By way ofbackground, FIG. 1 illustrates a schematic block diagram of an exemplarycomputing device 100 in accordance with embodiments of the presentinvention and/or with which those embodiments may be enabled orpracticed. It should be understood that FIG. 1 is provided as anon-limiting example.

The computing device 100, for example, may be implemented via firmware(e.g., an application-specific integrated circuit), hardware, or acombination of software, firmware, and hardware. It will be appreciatedthat each of the servers, controllers, switches, gateways, engines,and/or modules in the following figures (which collectively may bereferred to as servers or modules) may be implemented via one or more ofthe computing devices 100. As an example, the various servers may be aprocess running on one or more processors of one or more computingdevices 100, which may be executing computer program instructions andinteracting with other systems or modules in order to perform thevarious functionalities described herein. Unless otherwise specificallylimited, the functionality described in relation to a plurality ofcomputing devices may be integrated into a single computing device, orthe various functionalities described in relation to a single computingdevice may be distributed across several computing devices. Further, inrelation to the computing systems described in the followingfigures—such as, for example, the contact center system 200 of FIG.2—the various servers and computer devices thereof may be located onlocal computing devices 100 (i.e., on-site or at the same physicallocation as contact center agents), remote computing devices 100 (i.e.,off-site or in a cloud computing environment, for example, in a remotedata center connected to the contact center via a network), or somecombination thereof. Functionality provided by servers located onoff-site computing devices may be accessed and provided over a virtualprivate network (VPN), as if such servers were on-site, or thefunctionality may be provided using a software as a service (SaaS)accessed over the Internet using various protocols, such as byexchanging data via extensible markup language (XML), JSON, and thelike.

As shown in the illustrated example, the computing device 100 mayinclude a central processing unit (CPU) or processor 105 and a mainmemory 110. The computing device 100 may also include a storage device115, removable media interface 120, network interface 125, I/Ocontroller 130, and one or more input/output (I/O) devices 135, which asdepicted may include an, display device 135A, keyboard 135B, andpointing device 135C. The computing device 100 further may includeadditional elements, such as a memory port 140, a bridge 145, I/O ports,one or more additional input/output devices 135D, 135E, 135F, and acache memory 150 in communication with the processor 105.

The processor 105 may be any logic circuitry that responds to andprocesses instructions fetched from the main memory 110. For example,the process 105 may be implemented by an integrated circuit, e.g., amicroprocessor, microcontroller, or graphics processing unit, or in afield-programmable gate array or application-specific integratedcircuit. As depicted, the processor 105 may communicate directly withthe cache memory 150 via a secondary bus or backside bus. The cachememory 150 typically has a faster response time than main memory 110.The main memory 110 may be one or more memory chips capable of storingdata and allowing stored data to be directly accessed by the centralprocessing unit 105. The storage device 115 may provide storage for anoperating system, which controls scheduling tasks and access to systemresources, and other software. Unless otherwise limited, the computingdevice 100 may include an operating system and software capable ofperforming the functionality described herein.

As depicted in the illustrated example, the computing device 100 mayinclude a wide variety of I/O devices 135, one or more of which may beconnected via the I/O controller 130. Input devices, for example, mayinclude a keyboard 135B and a pointing device 135C, e.g., a mouse oroptical pen. Output devices, for example, may include video displaydevices, speakers, and printers. The I/O devices 135 and/or the I/Ocontroller 130 may include suitable hardware and/or software forenabling the use of multiple display devices. The computing device 100may also support one or more removable media interfaces 120, such as adisk drive, USB port, or any other device suitable for reading data fromor writing data to computer readable media. More generally, the I/Odevices 135 may include any conventional devices for performing thefunctionality described herein.

The computing device 100 may be any workstation, desktop computer,laptop or notebook computer, server machine, virtualized machine, mobileor smart phone, portable telecommunication device, media playing device,gaming system, mobile computing device, or any other type of computing,telecommunications or media device, without limitation, capable ofperforming the operations and functionality described herein.

Contact Center

With reference now to FIG. 2, a communications infrastructure or contactcenter system 200 is shown in accordance with exemplary embodiments ofthe present invention and/or with which exemplary embodiments of thepresent invention may be enabled or practiced. It should be understoodthat the term “contact center system” is used herein to refer to thesystem depicted in FIG. 2 and/or the components thereof, while the term“contact center” is used more generally to refer to contact centersystems, customer service providers operating those systems, and/or theorganizations or enterprises associated therewith. Thus, unlessotherwise specifically limited, the term “contact center” refersgenerally to a contact center system (such as the contact center system200), the associated customer service provider (such as a particularcustomer service provider providing customer services through thecontact center system 200), as well as the organization or enterprise onbehalf of which those customer services are being provided.

By way of background, customer service providers generally offer manytypes of services through contact centers. Such contact centers may bestaffed with employees or customer service agents (or simply “agents”),with the agents serving as an interface between a company, enterprise,government agency, or organization (hereinafter referred tointerchangeably as an “organization” or “enterprise”) and persons, suchas users, individuals, or customers (hereinafter referred tointerchangeably as “individuals” or “customers”). For example, theagents at a contact center may assist customers in making purchasingdecisions, receiving orders, or solving problems with products orservices already received. Within a contact center, such interactionsbetween contact center agents and outside entities or customers may beconducted over a variety of communication channels, such as, forexample, via voice (e.g., telephone calls or voice over IP or VoIPcalls), video (e.g., video conferencing), text (e.g., emails and textchat), screen sharing, co-browsing, or the like.

Operationally, contact centers generally strive to provide qualityservices to customers while minimizing costs. For example, one way for acontact center to operate is to handle every customer interaction with alive agent. While this approach may score well in terms of the servicequality, it likely would also be prohibitively expensive due to the highcost of agent labor. Because of this, most contact centers utilize somelevel of automated processes in place of live agents, such as, forexample, interactive voice response (IVR) systems, interactive mediaresponse (IMR) systems, internet robots or “bots”, automated chatmodules or “chatbots”, and the like. In many cases this has proven to bea successful strategy, as automated processes can be highly efficient inhandling certain types of interactions and effective at decreasing theneed for live agents. Such automation allows contact centers to targetthe use of human agents for the more difficult customer interactions,while the automated processes handle the more repetitive or routinetasks. Further, automated processes can be structured in a way thatoptimizes efficiency and promotes repeatability. Whereas a human or liveagent may forget to ask certain questions or follow-up on particulardetails, such mistakes are typically avoided through the use ofautomated processes. While customer service providers are increasinglyrelying on automated processes to interact with customers, the use ofsuch technologies by customers remains far less developed. Thus, whileIVR systems, IMR systems, and/or bots are used to automate portions ofthe interaction on the contact center-side of an interaction, theactions on the customer-side remain for the customer to performmanually.

Referring specifically to FIG. 2, the contact center system 200 may beused by a customer service provider to provide various types of servicesto customers. For example, the contact center system 200 may be used toengage and manage interactions in which automated processes (or bots) orhuman agents communicate with customers. As should be understood, thecontact center system 200 may be an in-house facility to a business orenterprise for performing the functions of sales and customer servicerelative to products and services available through the enterprise. Inanother aspect, the contact center system 200 may be operated by athird-party service provider that contracts to provide services foranother organization. Further, the contact center system 200 may bedeployed on equipment dedicated to the enterprise or third-party serviceprovider, and/or deployed in a remote computing environment such as, forexample, a private or public cloud environment with infrastructure forsupporting multiple contact centers for multiple enterprises. Thecontact center system 200 may include software applications or programs,which may be executed on premises or remotely or some combinationthereof. It should further be appreciated that the various components ofthe contact center system 200 may be distributed across variousgeographic locations and not necessarily contained in a single locationor computing environment.

It should further be understood that, unless otherwise specificallylimited, any of the computing elements of the present invention may beimplemented in cloud-based or cloud computing environments. As usedherein, “cloud computing”—or, simply, the “cloud”—is defined as a modelfor enabling ubiquitous, convenient, on-demand network access to ashared pool of configurable computing resources (e.g., networks,servers, storage, applications, and services) that can be rapidlyprovisioned via virtualization and released with minimal managementeffort or service provider interaction, and then scaled accordingly.Cloud computing can be composed of various characteristics (e.g.,on-demand self-service, broad network access, resource pooling, rapidelasticity, measured service, etc.), service models (e.g., Software as aService (“SaaS”), Platform as a Service (“PaaS”), Infrastructure as aService (“IaaS”), and deployment models (e.g., private cloud, communitycloud, public cloud, hybrid cloud, etc.). Often referred to as a“serverless architecture”, a cloud execution model generally includes aservice provider dynamically managing an allocation and provisioning ofremote servers for achieving a desired functionality.

In accordance with the illustrated example of FIG. 2, the components ormodules of the contact center system 200 may include: a plurality ofcustomer devices 205A, 205B, 205C; communications network (or simply“network”) 210; switch/media gateway 212; call controller 214;interactive media response (IMR) server 216; routing server 218; storagedevice 220; statistics (or “stat”) server 226; plurality of agentdevices 230A, 230B, 230C that include workbins 232A, 232B, 232C,respectively; multimedia/social media server 234; knowledge managementserver 236 coupled to a knowledge system 238; chat server 240; webservers 242; interaction (or “iXn”) server 244; universal contact server(or “UCS”) 246; reporting server 248; media services server 249; andanalytics module 250. It should be understood that any of thecomputer-implemented components, modules, or servers described inrelation to FIG. 2 or in any of the following figures may be implementedvia types of computing devices, such as, for example, the computingdevice 100 of FIG. 1. As will be seen, the contact center system 200generally manages resources (e.g., personnel, computers,telecommunication equipment, etc.) to enable delivery of services viatelephone, email, chat, or other communication mechanisms. Such servicesmay vary depending on the type of contact center and, for example, mayinclude customer service, help desk functionality, emergency response,telemarketing, order taking, and the like.

Customers desiring to receive services from the contact center system200 may initiate inbound communications (e.g., telephone calls, emails,chats, etc.) to the contact center system 200 via a customer device 205.While FIG. 2 shows three such customer devices—i.e., customer devices205A, 205B, and 205C—it should be understood that any number may bepresent. The customer devices 205, for example, may be a communicationdevice, such as a telephone, smart phone, computer, tablet, or laptop.In accordance with functionality described herein, customers maygenerally use the customer devices 205 to initiate, manage, and conductcommunications with the contact center system 200, such as telephonecalls, emails, chats, text messages, web-browsing sessions, and othermulti-media transactions.

Inbound and outbound communications from and to the customer devices 205may traverse the network 210, with the nature of network typicallydepending on the type of customer device being used and form ofcommunication. As an example, the network 210 may include acommunication network of telephone, cellular, and/or data services. Thenetwork 210 may be a private or public switched telephone network(PSTN), local area network (LAN), private wide area network (WAN),and/or public WAN such as the Internet.

In regard to the switch/media gateway 212, it may be coupled to thenetwork 210 for receiving and transmitting telephone calls betweencustomers and the contact center system 200. The switch/media gateway212 may include a telephone or communication switch configured tofunction as a central switch for agent level routing within the center.The switch may be a hardware switching system or implemented viasoftware. For example, the switch 215 may include an automatic calldistributor, a private branch exchange (PBX), an IP-based softwareswitch, and/or any other switch with specialized hardware and softwareconfigured to receive Internet-sourced interactions and/or telephonenetwork-sourced interactions from a customer, and route thoseinteractions to, for example, one of the agent devices 230. Thus, ingeneral, the switch/media gateway 212 establishes a voice connectionbetween the customer and the agent by establishing a connection betweenthe customer device 205 and agent device 230.

As further shown, the switch/media gateway 212 may be coupled to thecall controller 214 which, for example, serves as an adapter orinterface between the switch and the other routing, monitoring, andcommunication-handling components of the contact center system 200. Thecall controller 214 may be configured to process PSTN calls, VoIP calls,etc. For example, the call controller 214 may include computer-telephoneintegration (CTI) software for interfacing with the switch/media gatewayand other components. The call controller 214 may include a sessioninitiation protocol (SIP) server for processing SIP calls. The callcontroller 214 may also extract data about an incoming interaction, suchas the customer's telephone number, IP address, or email address, andthen communicate these with other contact center components inprocessing the interaction.

In regard to the interactive media response (IMR) server 216, it may beconfigured to enable self-help or virtual assistant functionality.Specifically, the IMR server 216 may be similar to an interactive voiceresponse (IVR) server, except that the IMR server 216 is not restrictedto voice and may also cover a variety of media channels. In an exampleillustrating voice, the IMR server 216 may be configured with an IMRscript for querying customers on their needs. For example, a contactcenter for a bank may tell customers via the IMR script to “press 1” ifthey wish to retrieve their account balance. Through continuedinteraction with the IMR server 216, customers may receive servicewithout needing to speak with an agent. The IMR server 216 may also beconfigured to ascertain why a customer is contacting the contact centerso that the communication may be routed to the appropriate resource.

In regard to the routing server 218, it may function to route incominginteractions. For example, once it is determined that an inboundcommunication should be handled by a human agent, functionality withinthe routing server 218 may select the most appropriate agent and routethe communication thereto. This agent selection may be based on whichavailable agent is best suited for handling the communication. Morespecifically, the selection of appropriate agent may be based on arouting strategy or algorithm that is implemented by the routing server218. In doing this, the routing server 218 may query data that isrelevant to the incoming interaction, for example, data relating to theparticular customer, available agents, and the type of interaction,which, as described more below, may be stored in particular databases.Once the agent is selected, the routing server 218 may interact with thecall controller 214 to route (i.e., connect) the incoming interaction tothe corresponding agent device 230. As part of this connection,information about the customer may be provided to the selected agent viatheir agent device 230. This information is intended to enhance theservice the agent is able to provide to the customer.

Regarding data storage, the contact center system 200 may include one ormore mass storage devices—represented generally by the storage device220—for storing data in one or more databases relevant to thefunctioning of the contact center. For example, the storage device 220may store customer data that is maintained in a customer database 222.Such customer data may include customer profiles, contact information,service level agreement (SLA), and interaction history (e.g., details ofprevious interactions with a particular customer, including the natureof previous interactions, disposition data, wait time, handle time, andactions taken by the contact center to resolve customer issues). Asanother example, the storage device 220 may store agent data in an agentdatabase 223. Agent data maintained by the contact center system 200 mayinclude agent availability and agent profiles, schedules, skills, handletime, etc. As another example, the storage device 220 may storeinteraction data in an interaction database 224. Interaction data mayinclude data relating to numerous past interactions between customersand contact centers. More generally, it should be understood that,unless otherwise specified, the storage device 220 may be configured toinclude databases and/or store data related to any of the types ofinformation described herein, with those databases and/or data beingaccessible to the other modules or servers of the contact center system200 in ways that facilitate the functionality described herein. Forexample, the servers or modules of the contact center system 200 mayquery such databases to retrieve data stored therewithin or transmitdata thereto for storage. The storage device 220, for example, may takethe form of any conventional storage medium and may be locally housed oroperated from a remote location.

In regard to the stat server 226, it may be configured to record andaggregate data relating to the performance and operational aspects ofthe contact center system 200. Such information may be compiled by thestat server 226 and made available to other servers and modules, such asthe reporting server 248, which then may use the data to produce reportsthat are used to manage operational aspects of the contact center andexecute automated actions in accordance with functionality describedherein. Such data may relate to the state of contact center resources,e.g., average wait time, abandonment rate, agent occupancy, and othersas functionality described herein would require.

The agent devices 230 of the contact center 200 may be communicationdevices configured to interact with the various components and modulesof the contact center system 200 in ways that facilitate functionalitydescribed herein. An agent device 230, for example, may include atelephone adapted for regular telephone calls or VoIP calls. An agentdevice 230 may further include a computing device configured tocommunicate with the servers of the contact center system 200, performdata processing associated with operations, and interface with customersvia voice, chat, email, and other multimedia communication mechanismsaccording to functionality described herein. While FIG. 2 shows threesuch agent devices—i.e., agent devices 230A, 230B and 230C—it should beunderstood that any number may be present.

In regard to the multimedia/social media server 234, it may beconfigured to facilitate media interactions (other than voice) with thecustomer devices 205 and/or the servers 242. Such media interactions maybe related, for example, to email, voice mail, chat, video,text-messaging, web, social media, co-browsing, etc. Themulti-media/social media server 234 may take the form of any IP routerconventional in the art with specialized hardware and software forreceiving, processing, and forwarding multi-media events andcommunications.

In regard to the knowledge management server 234, it may be configuredfacilitate interactions between customers and the knowledge system 238.In general, the knowledge system 238 may be a computer system capable ofreceiving questions or queries and providing answers in response. Theknowledge system 238 may be included as part of the contact centersystem 200 or operated remotely by a third party. The knowledge system238 may include an artificially intelligent computer system capable ofanswering questions posed in natural language by retrieving informationfrom information sources such as encyclopedias, dictionaries, newswirearticles, literary works, or other documents submitted to the knowledgesystem 238 as reference materials, as is known in the art. As anexample, the knowledge system 238 may be embodied as IBM Watson or alike system.

In regard to the chat server 240, it may be configured to conduct,orchestrate, and manage electronic chat communications with customers.In general, the chat server 240 is configured to implement and maintainchat conversations and generate chat transcripts. Such chatcommunications may be conducted by the chat server 240 in such a waythat a customer communicates with automated chatbots, human agents, orboth. In exemplary embodiments, the chat server 240 may perform as achat orchestration server that dispatches chat conversations among thechatbots and available human agents. In such cases, the processing logicof the chat server 240 may be rules driven so to leverage an intelligentworkload distribution among available chat resources. The chat server240 further may implement, manage and facilitate user interfaces (alsoUIs) associated with the chat feature, including those UIs generated ateither the customer device 205 or the agent device 230. The chat server240 may be configured to transfer chats within a single chat sessionwith a particular customer between automated and human sources suchthat, for example, a chat session transfers from a chatbot to a humanagent or from a human agent to a chatbot. The chat server 240 may alsobe coupled to the knowledge management server 234 and the knowledgesystems 238 for receiving suggestions and answers to queries posed bycustomers during a chat so that, for example, links to relevant articlescan be provided.

In regard to the web servers 242, such servers may be included toprovide site hosts for a variety of social interaction sites to whichcustomers subscribe, such as Facebook, Twitter, Instagram, etc. Thoughdepicted as part of the contact center system 200, it should beunderstood that the web servers 242 may be provided by third partiesand/or maintained remotely. The web servers 242 may also providewebpages for the enterprise or organization being supported by thecontact center system 200. For example, customers may browse thewebpages and receive information about the products and services of aparticular enterprise. Within such enterprise webpages, mechanisms maybe provided for initiating an interaction with the contact center system200, for example, via web chat, voice, or email. An example of such amechanism is a widget, which can be deployed on the webpages or websiteshosted on the web servers 242. As used herein, a widget refers to a userinterface component that performs a particular function. In someimplementations, a widget may include a graphical user interface controlthat can be overlaid on a webpage displayed to a customer via theInternet. The widget may show information, such as in a window or textbox, or include buttons or other controls that allow the customer toaccess certain functionalities, such as sharing or opening a file orinitiating a communication. In some implementations, a widget includes auser interface component having a portable portion of code that can beinstalled and executed within a separate webpage without compilation.Some widgets can include corresponding or additional user interfaces andbe configured to access a variety of local resources (e.g., a calendaror contact information on the customer device) or remote resources vianetwork (e.g., instant messaging, electronic mail, or social networkingupdates).

In regard to the interaction (iXn) server 244, it may be configured tomanage deferrable activities of the contact center and the routingthereof to human agents for completion. As used herein, deferrableactivities include back-office work that can be performed off-line,e.g., responding to emails, attending training, and other activitiesthat do not entail real-time communication with a customer. As anexample, the interaction (iXn) server 244 may be configured to interactwith the routing server 218 for selecting an appropriate agent to handleeach of the deferable activities. Once assigned to a particular agent,the deferable activity is pushed to that agent so that it appears on theagent device 230 of the selected agent. The deferable activity mayappear in a workbin 232 as a task for the selected agent to complete.The functionality of the workbin 232 may be implemented via anyconventional data structure, such as, for example, a linked list, array,etc. Each of the agent devices 230 may include a workbin 232, with theworkbins 232A, 232B, and 232C being maintained in the agent devices230A, 230B, and 230C, respectively. As an example, a workbin 232 may bemaintained in the buffer memory of the corresponding agent device 230.

In regard to the universal contact server (UCS) 246, it may beconfigured to retrieve information stored in the customer database 222and/or transmit information thereto for storage therein. For example,the UCS 246 may be utilized as part of the chat feature to facilitatemaintaining a history on how chats with a particular customer werehandled, which then may be used as a reference for how future chatsshould be handled. More generally, the UCS 246 may be configured tofacilitate maintaining a history of customer preferences, such aspreferred media channels and best times to contact. To do this, the UCS246 may be configured to identify data pertinent to the interactionhistory for each customer such as, for example, data related to commentsfrom agents, customer communication history, and the like. Each of thesedata types then may be stored in the customer database 222 or on othermodules and retrieved as functionality described herein requires.

In regard to the reporting server 248, it may be configured to generatereports from data compiled and aggregated by the statistics server 226or other sources. Such reports may include near real-time reports orhistorical reports and concern the state of contact center resources andperformance characteristics, such as, for example, average wait time,abandonment rate, agent occupancy. The reports may be generatedautomatically or in response to specific requests from a requestor(e.g., agent, administrator, contact center application, etc.). Thereports then may be used toward managing the contact center operationsin accordance with functionality described herein.

In regard to the media services server 249, it may be configured toprovide audio and/or video services to support contact center features.In accordance with functionality described herein, such features mayinclude prompts for an IVR or IMR system (e.g., playback of audiofiles), hold music, voicemails/single party recordings, multi-partyrecordings (e.g., of audio and/or video calls), speech recognition, dualtone multi frequency (DTMF) recognition, faxes, audio and videotranscoding, secure real-time transport protocol (SRTP), audioconferencing, video conferencing, coaching (e.g., support for a coach tolisten in on an interaction between a customer and an agent and for thecoach to provide comments to the agent without the customer hearing thecomments), call analysis, keyword spotting, and the like.

In regard to the analytics module 250, it may be configured to providesystems and methods for performing analytics on data received from aplurality of different data sources as functionality described hereinmay require. In accordance with example embodiments, the analyticsmodule 250 also may generate, update, train, and modify predictors ormodels 252 based on collected data, such as, for example, customer data,agent data, and interaction data. The models 252 may include behaviormodels of customers or agents. The behavior models may be used topredict behaviors of, for example, customers or agents, in a variety ofsituations, thereby allowing embodiments of the present invention totailor interactions based on such predictions or to allocate resourcesin preparation for predicted characteristics of future interactions,thereby improving overall contact center performance and the customerexperience. It will be appreciated that, while the analytics module 250is depicted as being part of a contact center, such behavior models alsomay be implemented on customer systems (or, as also used herein, on the“customer-side” of the interaction) and used for the benefit ofcustomers.

According to exemplary embodiments, the analytics module 250 may haveaccess to the data stored in the storage device 220, including thecustomer database 222 and agent database 223. The analytics module 250also may have access to the interaction database 224, which stores datarelated to interactions and interaction content (e.g., transcripts ofthe interactions and events detected therein), interaction metadata(e.g., customer identifier, agent identifier, medium of interaction,length of interaction, interaction start and end time, department,tagged categories), and the application setting (e.g., the interactionpath through the contact center). Further, as discussed more below, theanalytic module 250 may be configured to retrieve data stored within thestorage device 220 for use in developing and training algorithms andmodels 252, for example, by applying machine learning techniques.

One or more of the included models 252 may be configured to predictcustomer or agent behavior and/or aspects related to contact centeroperation and performance. Further, one or more of the models 252 may beused in natural language processing and, for example, include intentrecognition and the like. The models 252 may be developed based upon 1)known first principle equations describing a system, 2) data, resultingin an empirical model, or 3) a combination of known first principleequations and data. In developing a model for use with presentembodiments, because first principles equations are often not availableor easily derived, it may be generally preferred to build an empiricalmodel based upon collected and stored data. To properly capture therelationship between the manipulated/disturbance variables and thecontrolled variables of complex systems, it may be preferable that themodels 252 are nonlinear. This is because nonlinear models can representcurved rather than straight-line relationships betweenmanipulated/disturbance variables and controlled variables, which arecommon to complex systems such as those discussed herein. Given theforegoing requirements, a machine learning or neural network-basedapproach is presently a preferred embodiment for implementing the models252. Neural networks, for example, may be developed based upon empiricaldata using advanced regression algorithms.

The analytics module 250 may further include an optimizer 254. As willbe appreciated, an optimizer may be used to minimize a “cost function”subject to a set of constraints, where the cost function is amathematical representation of desired objectives or system operation.Because the models 252 may be non-linear, the optimizer 254 may be anonlinear programming optimizer. It is contemplated, however, that thepresent invention may be implemented by using, individually or incombination, a variety of different types of optimization approaches,including, but not limited to, linear programming, quadraticprogramming, mixed integer non-linear programming, stochasticprogramming, global non-linear programming, genetic algorithms,particle/swarm techniques, and the like.

According to exemplary embodiments, the models 252 and the optimizer 254may together be used within an optimization system 255. For example, theanalytics module 250 may utilize the optimization system 255 as part ofan optimization process by which aspects of contact center performanceand operation are optimized or, at least, enhanced. This, for example,may include aspects related to the customer experience, agentexperience, interaction routing, natural language processing, intentrecognition, or other functionality related to automated processes.

Chat Systems

Turning to FIGS. 3, 4 and 5, various aspects of chat systems andchatbots are shown. As will be seen, present embodiments may include orbe enabled by such chat features, which, in general, enable the exchangeof text messages between different parties. Those parties may includelive persons, such as customers and agents, as well as automatedprocesses, such as bots or chatbots.

By way of background, a bot (also known as an “Internet bot”) is asoftware application that runs automated tasks or scripts over theInternet. Typically, bots perform tasks that are both simple andstructurally repetitive at a much higher rate than would be possible fora person. A chatbot is a particular type of bot and, as used herein, isdefined as a piece of software and/or hardware that conducts aconversation via auditory or textual methods. As will be appreciated,chatbots are often designed to convincingly simulate how a human wouldbehave as a conversational partner. Chatbots are typically used indialog systems for various practical purposes including customer serviceor information acquisition. Some chatbots use sophisticated naturallanguage processing systems, while simpler ones scan for keywords withinthe input and then select a reply from a database based on matchingkeywords or wording pattern.

Before proceeding further with the description of the present invention,an explanatory note will be provided in regard to referencing systemcomponents—e.g., modules, servers, and other components—that havealready been introduced in any previous figure. Whether or not thesubsequent reference includes the corresponding numerical identifiersused in the previous figures, it should be understood that the referenceincorporates the example described in the previous figures and, unlessotherwise specifically limited, may be implemented in accordance witheither that examples or other conventional technology capable offulfilling the desired functionality, as would be understood by one ofordinary skill in the art. Thus, for example, subsequent mention of a“contact center system” should be understood as referring to theexemplary “contact center system 200” of FIG. 2 and/or otherconventional technologies for implementing a contact center system. Asadditional examples, a subsequent mention below to a “customer device”,“agent device”, “chat server”, or “computing device” should beunderstood as referring to the exemplary “customer device 205”, “agentdevice 230”, “chat server 240”, or “computing device 200”, respectively,of FIGS. 1-2, as well as conventional technology for fulfilling the samefunctionality.

Chat features and chatbots will now be discussed in greater specificitywith reference to the exemplary embodiments of a chat server, chatbot,and chat interface depicted, respectively, in FIGS. 3, 4, and 5. Whilethese examples are provided with respect to chat systems implemented onthe contact center-side, such chat systems may be used on thecustomer-side of an interaction. Thus, it should be understood that theexemplary chat systems of FIGS. 3, 4, and 5 may be modified foranalogous customer-side implementation, including the use ofcustomer-side chatbots configured to interact with agents and chatbotsof contact centers on a customer's behalf. It should further beunderstood that chat features may be utilized by voice communicationsvia converting text-to-speech and/or speech-to-text.

Referring specifically now to FIG. 3, a more detailed block diagram isprovided of a chat server 240, which may be used to implement chatsystems and features. The chat server 240 may be coupled to (i.e., inelectronic communication with) a customer device 205 operated by thecustomer over a data communications network 210. The chat server 240,for example, may be operated by a enterprise as part of a contact centerfor implementing and orchestrating chat conversations with thecustomers, including both automated chats and chats with human agents.In regard to automated chats, the chat server 240 may host chatautomation modules or chatbots 260A-260C (collectively referenced as260), which are configured with computer program instructions forengaging in chat conversations. Thus, generally, the chat server 240implements chat functionality, including the exchange of text-based orchat communications between a customer device 205 and an agent device230 or a chatbot 260. As discussed more below, the chat server 240 mayinclude a customer interface module 265 and agent interface module 266for generating particular UIs at the customer device 205 and the agentdevice 230, respectively, that facilitate chat functionality.

In regard to the chatbots 260, each can operate as an executable programthat is launched according to demand. For example, the chat server 240may operate as an execution engine for the chatbots 260, analogous toloading VoiceXML files to a media server for interactive voice response(IVR) functionality. Loading and unloading may be controlled by the chatserver 240, analogous to how a VoiceXML script may be controlled in thecontext of an interactive voice response. The chat server 240 mayfurther provide a means for capturing and collecting customer data in aunified way, similar to customer data capturing in the context of IVR.Such data can be stored, shared, and utilized in a subsequentconversation, whether with the same chatbot, a different chatbot, anagent chat, or even a different media type. In example embodiments, thechat server 240 is configured to orchestrate the sharing of data amongthe various chatbots 260 as interactions are transferred or transitionedover from one chatbot to another or from one chatbot to a human agent.The data captured during interaction with a particular chatbot may betransferred along with a request to invoke a second chatbot or humanagent.

In exemplary embodiments, the number of chatbots 260 may vary accordingto the design and function of the chat server 240 and is not limited tothe number illustrated in FIG. 3. Further, different chatbots may becreated to have different profiles, which can then be selected betweento match the subject matter of a particular chat or a particularcustomer. For example, the profile of a particular chatbot may includeexpertise for helping a customer on a particular subject orcommunication style aimed at a certain customer preference. Morespecifically, one chatbot may be designed to engage in a first topic ofcommunication (e.g., opening a new account with the business), whileanother chatbot may be designed to engage in a second topic ofcommunication (e.g., technical support for a product or service providedby the business). Or, chatbots may be configured to utilize differentdialects or slang or have different personality traits orcharacteristics. Engaging chatbots with profiles that are catered tospecific types of customers may enable more effective communication andresults. The chatbot profiles may be selected based on information knownabout the other party, such as demographic information, interactionhistory, or data available on social media. The chat server 240 may hosta default chatbot that is invoked if there is insufficient informationabout the customer to invoke a more specialized chatbot. Optionally, thedifferent chatbots may be customer selectable. In exemplary embodiments,profiles of chatbots 260 may be stored in a profile database hosted inthe storage device 220. Such profiles may include the chatbot'spersonality, demographics, areas of expertise, and the like.

The customer interface module 265 and agent interface module 266 may beconfigured to generating user interfaces (UIs) for display on thecustomer device 205 that facilitate chat communications between thecustomer and a chatbot 260 or human agent. Likewise, an agent interfacemodule 266 may generate particular UIs on the agent device 230 thatfacilitate chat communications between an agent operating an agentdevice 230 and the customer. The agent interface module 266 may alsogenerate UIs on an agent device 230 that allow an agent to monitoraspects of an ongoing chat between a chatbot 260 and a customer. Forexample, the customer interface module 265 may transmit signals to thecustomer device 205 during a chat session that are configured togenerated particular UIs on the customer device 205, which may includethe display of the text messages being sent from the chatbot 260 orhuman agent as well as other non-text graphics that are intended toaccompany the text messages, such as emoticons or animations. Similarly,the agent interface module 266 may transmit signals to the agent device230 during a chat session that are configured to generated UIs on theagent device 230. Such UIs may include an interface that facilitates theagent selection of non-text graphics for accompanying outgoing textmessages to customers.

In exemplary embodiments, the chat server 240 may be implemented in alayered architecture, with a media layer, a media control layer, and thechatbots executed by way of the IMR server 216 (similar to executing aVoiceXML on an IVR media server). As described above, the chat server240 may be configured to interact with the knowledge management server234 to query the server for knowledge information. The query, forexample, may be based on a question received from the customer during achat. Responses received from the knowledge management server 234 maythen be provided to the customer as part of a chat response.

Referring specifically now to FIG. 4, a block diagram is provided of anexemplary chat automation module or chatbot 260. As illustrated, thechatbot 260 may include several modules, including a text analyticsmodule 270, dialog manager 272, and output generator 274. It will beappreciated that, in a more detailed discussion of chatbot operability,other subsystems or modules may be described, including, for examples,modules related to intent recognition, text-to-speech or speech-to-textmodules, as well as modules related to script storage, retrieval, anddata field processing in accordance with information stored in agent orcustomer profiles. Such topics, however, are covered more completely inother areas of this disclosure—for example, in relation to FIGS. 6 and7—and so will not be repeated here. It should nevertheless be understoodthat the disclosures made in these areas may be used in analogous waystoward chatbot operability in accordance with functionality describedherein.

The text analytics module 270 may be configured to analyze andunderstand natural language. In this regard, the text analytics modulemay be configured with a lexicon of the language, syntactic/semanticparser, and grammar rules for breaking a phrase provided by the customerdevice 205 into an internal syntactic and semantic representation. Theconfiguration of the text analytics module depends on the particularprofile associated with the chatbot. For example, certain words may beincluded in the lexicon for one chatbot but excluded that of another.

The dialog manager 272 receives the syntactic and semanticrepresentation from the text analytics module 270 and manages thegeneral flow of the conversation based on a set of decision rules. Inthis regard, the dialog manager 272 maintains a history and state of theconversation and, based on those, generates an outbound communication.The communication may follow the script of a particular conversationpath selected by the dialog manager 272. As described in further detailbelow, the conversation path may be selected based on an understandingof a particular purpose or topic of the conversation. The script for theconversation path may be generated using any of various languages andframeworks conventional in the art, such as, for example, artificialintelligence markup language (AIML), SCXML, or the like.

During the chat conversation, the dialog manager 272 selects a responsedeemed to be appropriate at the particular point of the conversationflow/script and outputs the response to the output generator 274. Inexemplary embodiments, the dialog manager 272 may also be configured tocompute a confidence level for the selected response and provide theconfidence level to the agent device 230. Every segment, step, or inputin a chat communication may have a corresponding list of possibleresponses. Responses may be categorized based on topics (determinedusing a suitable text analytics and topic detection scheme) andsuggested next actions are assigned. Actions may include, for example,responses with answers, additional questions, transfer to a human agentto assist, and the like. The confidence level may be utilized to assistthe system with deciding whether the detection, analysis, and responseto the customer input is appropriate or whether a human agent should beinvolved. For example, a threshold confidence level may be assigned toinvoke human agent intervention based on one or more business rules. Inexemplary embodiments, confidence level may be determined based oncustomer feedback. As described, the response selected by the dialogmanager 272 may include information provided by the knowledge managementserver 234.

In exemplary embodiments, the output generator 274 takes the semanticrepresentation of the response provided by the dialog manager 272, mapsthe response to a chatbot profile or personality (e.g., by adjusting thelanguage of the response according to the dialect, vocabulary, orpersonality of the chatbot), and outputs an output text to be displayedat the customer device 205. The output text may be intentionallypresented such that the customer interacting with a chatbot is unawarethat it is interacting with an automated process as opposed to a humanagent. As will be seen, in accordance with other embodiments, the outputtext may be linked with visual representations, such as emoticons oranimations, integrated into the customer's user interface.

Reference will now be made to FIG. 5, in which a webpage 280 having anexemplary implementation of a chat feature 282 is presented. The webpage280, for example, may be associated with an enterprise website andintended to initiate interaction between prospective or currentcustomers visiting the webpage and a contact center associated with theenterprise. As will be appreciated, the chat feature 282 may begenerated on any type of customer device 205, including personalcomputing devices such as laptops, tablet devices, or smart phones.Further, the chat feature 282 may be generated as a window within awebpage or implemented as a full-screen interface. As in the exampleshown, the chat feature 282 may be contained within a defined portion ofthe webpage 280 and, for example, may be implemented as a widget via thesystems and components described above and/or any other conventionalmeans. In general, the chat feature 282 may include an exemplary way forcustomers to enter text messages for delivery to a contact center.

As an example, the webpage 280 may be accessed by a customer via acustomer device, such as the customer device, which provides acommunication channel for chatting with chatbots or live agents. Inexemplary embodiments, as shown, the chat feature 282 includesgenerating a user interface, which is referred to herein as a customerchat interface 284, on a display of the customer device. The customerchat interface 284, for example, may be generated by the customerinterface module of a chat server, such as the chat server, as alreadydescribed. As described, the customer interface module 265 may sendsignals to the customer device 205 that are configured to generate thedesired customer chat interface 284, for example, in accordance with thecontent of a chat message issued by a chat source, which, in theexample, is a chatbot or agent named “Kate”. The customer chat interface284 may be contained within a designated area or window, with thatwindow covering a designated portion of the webpage 280. The customerchat interface 284 also may include a text display area 286, which isthe area dedicated to the chronological display of received and senttext messages. The customer chat interface 284 further includes a textinput area 288, which is the designated area in which the customerinputs the text of their next message. As will be appreciated, otherconfigurations are also possible.

Customer Automation Systems

Embodiments of the present invention include systems and methods forautomating and augmenting customer actions during various stages ofinteraction with a customer service provider or contact center. As willbe seen, those various stages of interaction may be classified aspre-contact, during-contact, and post-contact stages (or, respectively,pre-interaction, during-interaction, and post-interaction stages). Withspecific reference now to FIG. 6, an exemplary customer automationsystem 300 is shown that may be used with embodiments of the presentinvention. To better explain how the customer automation system 300functions, reference will also be made to FIG. 7, which provides aflowchart 350 of an exemplary method for automating customer actionswhen, for example, the customer interacts with a contact center.Additional information related to customer automation are provided inU.S. application Ser. No. 16/151,362, filed on Oct. 4, 2018, entitled“System and Method for Customer Experience Automation”, the content ofwhich is incorporated herein by reference.

The customer automation system 300 of FIG. 6 represents a system thatmay be generally used for customer-side automations, which, as usedherein, refers to the automation of actions taken on behalf of acustomer in interactions with customer service providers or contactcenters. Such interactions may also be referred to as “customer-contactcenter interactions” or simply “customer interactions”. Further, indiscussing such customer-contact center interactions, it should beappreciated that reference to a “contact center” or “customer serviceprovider” is intended to generally refer to any customer servicedepartment or other service provider associated with an organization orenterprise (such as, for example, a business, governmental agency,non-profit, school, etc.) with which a user or customer has business,transactions, affairs or other interests.

In exemplary embodiments, the customer automation system 300 may beimplemented as a software program or application running on a mobiledevice or other computing device, cloud computing devices (e.g.,computer servers connected to the customer device 205 over a network),or combinations thereof (e.g., some modules of the system areimplemented in the local application while other modules are implementedin the cloud. For the sake of convenience, embodiments are primarilydescribed in the context of implementation via an application running onthe customer device 205. However, it should be understood that presentembodiments are not limited thereto.

The customer automation system 300 may include several components ormodules. In the illustrated example of FIG. 6, the customer automationsystem 300 includes a user interface 305, natural language processing(NLP) module 310, intent inference module 315, script storage module320, script processing module 325, customer profile database or module(or simply “customer profile”) 330, communication manager module 335,text-to-speech module 340, speech-to-text module 342, and applicationprogramming interface (API) 345, each of which will be described withmore particularity with reference also to flowchart 350 of FIG. 7.

In an example of operation, with specific reference now to the flowchart350 of FIG. 7, the customer automation system 300 may receive input atan initial step or operation 355. Such input may come from severalsources. For example, a primary source of input may be the customer,where such input is received via the customer device. The input also mayinclude data received from other parties, particularly partiesinteracting with the customer through the customer device. For example,information or communications sent to the customer from the contactcenter may provide aspects of the input. In either case, the input maybe provided in the form of free speech or text (e.g., unstructured,natural language input). Input also may include other forms of datareceived or stored on the customer device.

Continuing with the flow diagram 350, at an operation 360, the customerautomation system 300 parses the natural language of the input using theNLP module 310 and, therefrom, infers a intent using the intentinference module 315. For example, where the input is provided as speechfrom the customer, the speech may be transcribed into text by aspeech-to-text system (such as a large vocabulary continuous speechrecognition or LVCSR system) as part of the parsing by the NLP module310. The transcription may be performed locally on the customer device205 or the speech may be transmitted over a network for conversion totext by a cloud-based server. In certain embodiments, for example, theintent inference module 315 may automatically infer the customer'sintent from the text of the provided input using artificial intelligenceor machine learning techniques. Such artificial intelligence techniquesmay include, for example, identifying one or more keywords from thecustomer input and searching a database of potential intentscorresponding to the given keywords. The database of potential intentsand the keywords corresponding to the intents may be automatically minedfrom a collection of historical interaction recordings. In cases wherethe customer automation system 300 fails to understand the intent fromthe input, a selection of several intents may be provided to thecustomer in the user interface 305. The customer may then clarify theirintent by selecting one of the alternatives or may request that otheralternatives be provided.

After the customer's intent is determined, the flowchart 350 proceeds toan operation 365 where the customer automation system 300 loads a scriptassociated with the given intent. Such scripts, for example, may bestored and retrieved from the script storage module 320. Such scriptsmay include a set of commands or operations, pre-written speech or text,and/or fields of parameters or data (also “data fields”), whichrepresent data that is required to automate an action for the customer.For example, the script may include commands, text, and data fields thatwill be needed in order to resolve the issue specified by the customer'sintent. Scripts may be specific to a particular contact center andtailored to resolve particular issues. Scripts may be organized in anumber of ways, for example, in a hierarchical fashion, such as whereall scripts pertaining to a particular organization are derived from acommon “parent” script that defines common features. The scripts may beproduced via mining data, actions, and dialogue from previous customerinteractions. Specifically, the sequences of statements made during arequest for resolution of a particular issue may be automatically minedfrom a collection of historical interactions between customers andcustomer service providers. Systems and methods may be employed forautomatically mining effective sequences of statements and comments, asdescribed from the contact center agent side, are described in U.S.patent application Ser. No. 14/153,049 “Computing Suggested Actions inCaller Agent Phone Calls By Using Real-Time Speech Analytics andReal-Time Desktop Analytics,” filed in the United States Patent andTrademark Office on Jan. 12, 2014, the entire disclosure of which isincorporated by reference herein.

With the script retrieved, the flowchart 350 proceeds to an operation370 where the customer automation system 300 processes or “loads” thescript. This action may be performed by the script processing module325, which performs it by filling in the data fields of the script withappropriate data pertaining to the customer. More specifically, thescript processing module 325 may extract customer data that is relevantto the anticipated interaction, with that relevance being predeterminedby the script selected as corresponding to the customer's intent. Thedata for many of the data fields within the script may be automaticallyloaded with data retrieved from data stored within the customer profile330. As will be appreciated, the customer profile 330 may storeparticular data related to the customer, for example, the customer'sname, birth date, address, account numbers, authentication information,and other types of information relevant to customer serviceinteractions. The data selected for storage within the customer profile330 may be based on data the customer has used in previous interactionsand/or include data values obtained directly by the customer. In case ofany ambiguity regarding the data fields or missing information within ascript, the script processing module 325 may include functionality thatprompts and allows the customer to manually input the neededinformation.

Referring again to the flowchart 350, at an operation 375, the loadedscript may be transmitted to the customer service provider or contactcenter. As discussed more below, the loaded script may include commandsand customer data necessary to automate at least a part of aninteraction with the contact center on the customer's behalf. Inexemplary embodiments, an API 345 is used so to interact with thecontact center directly. Contact centers may define a protocol formaking commonplace requests to their systems, which the API 345 isconfigured to do. Such APIs may be implemented over a variety ofstandard protocols such as Simple Object Access Protocol (SOAP) usingExtensible Markup Language (XML), a Representational State Transfer(REST) API with messages formatted using XML or JavaScript ObjectNotation (JSON), and the like. Accordingly, the customer automationsystem 300 may automatically generate a formatted message in accordancewith a defined protocol for communication with a contact center, wherethe message contains the information specified by the script inappropriate portions of the formatted message.

Personal Bot

With reference now to FIG. 8, an exemplary system 400 is shown thatincludes an automated personal assistant or, as referred to herein,personal bot 405. As will be seen, the personal bot 405 is configured toautomate aspects of interactions with a customer service provider onbehalf of a customer. As stated above, present invention relates tosystems and methods for automating aspects of the customer-side of theinteractions between customers and customer service providers or contactcenters. Accordingly, the personal bot 405 may provide ways to automateactions that customers are required to perform when contacting,interacting, or following up with contact centers.

The personal bot 405, as used herein, may generally reference anycustomer-side implementation of any of the automated processes orautomation functionality described herein. Thus, it should be understoodthat, unless otherwise specifically limited, the personal bot 405 maygenerally employ any of the technologies discussed herein—includingthose related to the chatbots 260 and the customer automation system300—to enable or enhance automation services available to customers. Forexample, as indicated in FIG. 8, the personal bot 405 may include thefunctionality of the above-described customer automation system 300.Additionally, the personal bot 405 may include a customer-sideimplementation of a chatbot (for example, the chatbot 260 of FIGS. 4 and5), which will be referred herein as a customer chatbot 410. As will beseen, the customer chatbot 410 may be configured to interact privatelywith the customer in order to obtain feedback and direction from thecustomer pertaining to actions related to ongoing, future, or pastinteractions with contact centers. Further, the customer chatbot 410 maybe configured to interact with live agents or chatbots associated with acontact center on behalf of the customer.

As shown in FIG. 8, in regard to system architecture, the personal bot405 may be implemented as a software program or application running on amobile device or personal computing device (shown as a customer device205) of the customer. For example, the personal bot 405A may includelocally stored modules, including the customer automation system 300,the customer chatbot 410, and elements of the customer profile 330A. Thepersonal bot 405 also may include remote or cloud computing components(e.g., one or more computer servers or modules connected to the customerdevice 205 over a network 210), which may be hosted in a cloud computingenvironment or cloud 415 (see cloud hosted elements of the personal bot405B). For example, as shown in the illustrated example, the scriptstorage module 320 and elements of the customer profile 330B may bestored in databases in the cloud 415. It should be understood, however,that present embodiments are not limited to this arrangement and, forexample, may include other components being implemented in the cloud415.

Accordingly, as will be seen, embodiments of the present inventioninclude systems and methods for automatically initiating and conductingan interaction with a contact center to resolve an issue on behalf of acustomer. Toward this objective, the personal bot 405 may be configuredto automate particular aspects of interactions with a contact center onbehalf of the customer. Several examples of these types of embodimentswill now be discussed in which resources described herein—including thecustomer automation system 300 and customer chatbot 410—are used toprovide the necessary automation. In presenting these embodiments,reference is again made to previously incorporated U.S. application Ser.No. 16/151,362, entitled “System and Method for Customer ExperienceAutomation”, which includes further background and other supportingmaterials.

Embodiments of the present invention include the personal bot 405 andrelated resources automating one or more actions or processes by whichthe customer initiates a communication with a contact center forinteracting therewith. As will be seen, this type of automation isprimarily aimed at those actions normally occurring within thepre-contact or pre-interaction stage of customer interactions.

For example, in accordance with an exemplary embodiment, when a customerchooses to contact a contact center, the customer automation system 300may automate the process of connecting the customer with the contactcenter. For example, present embodiments may automatically navigate anIVR system of a contact center on behalf of the customer using a loadedscript. Further, the customer automation system 300 may automaticallynavigate an IVR menu system for a customer, including, for example,authenticating the customer by providing authentication information(e.g., entering a customer number through dual-tone multi-frequency orDTMF or “touch tone” signaling or through text to speech synthesis) andselecting menu options (e.g., using DTMF signaling or through text tospeech synthesis) to reach the proper department associated with theinferred intent from the customer's input. More specifically, thecustomer profile 330 may include authentication information that wouldtypically be requested of customers accessing customer support systemssuch as usernames, account identifying information, personalidentification information (e.g., a social security number), and/oranswers to security questions. As additional examples, the customerautomation system 300 may have access to text messages and/or emailmessages sent to the customer's account on the customer device 205 inorder to access one-time passwords sent to the customer, and/or may haveaccess to a one-time password (OTP) generator stored locally on thecustomer device 205. Accordingly, embodiments of the present inventionmay be capable of automatically authenticating the customer with thecontact center prior to an interaction. In accordance with otherembodiments, the customer automation system 300 may automate a processfor preparing an agent before a call from a customer. For example, thecustomer automation system 300 may send a request that the agent studycertain materials provided by the customer before the live call happens.

Embodiments of the present invention further include the personal bot405 and related resources automating the actual interaction (or aspectsthereof) between the customer and a contact center. As will be seen,this type of automation is primarily aimed at those actions normallyoccurring within the during-contact or during-interaction stage ofcustomer interactions.

For example, the customer automation system 300 may interact withentities within a contact center on behalf of the customer. Withoutlimitation, such entities may include automated processes, such aschatbots, and live agents. Once connected to the contact center, thecustomer automation system 300 may retrieve a script from the scriptstorage module 320 that includes an interaction script (e.g., a dialoguetree). The interaction script may generally consist of a template ofstatements for the customer automation system 300 to make to an entitywithin the contact center, for example, a live agent. In exemplaryembodiments, the customer chatbot 410 may interact with the live agenton the customer's behalf in accordance with the interaction script. Asalready described, the interaction script (or simply “script”) mayconsist of a template having defined dialogue (i.e., predetermined textor statements) and data fields. As previously described, to “load” thescript, information or data pertinent to the customer is determined andloaded into the appropriate data fields. Such pertinent data may beretrieved from the customer profile 330 and/or derived from inputprovided by the customer through the customer interface 305. Accordingto certain embodiments, the customer chatbot 410 also may be used tointeract with the customer to prompt such input so that all of thenecessary data fields within the script are filled. In otherembodiments, the script processing module 325 may prompt the customer tosupply any missing information (e.g., information that is not availablefrom the customer profile 330) to fill in blanks in the template throughthe user interface 305 prior to initiating a communication with thecontact center. In certain embodiments, the script processing module 325may also request that the customer confirm the accuracy of all of theinformation that the customer automation system 300 will provide to thecontact center.

Once the loaded script is complete, for example, the interaction withthe live agent may begin with an initial statement explaining the reasonfor the call (e.g., “I am calling on behalf of your customer, Mr. ThomasAnderson, regarding what appears to be double billing.”), descriptionsof particular details related to the issue (e.g., “In the previous threemonths, his bill was approximately fifty dollars. However, his mostrecent bill was for one hundred dollars.”), and the like. While suchstatements may be provided in text to the contact center, it may also beprovided in voice, for example, when interacting with a live agent. Inregard to how such an embodiment may function, a speech synthesizer ortext-to-speech module 340 may be used to generate speech to betransmitted to the contact center agent over a voice communicationchannel. Further, speech received from the agent of the contact centermay be converted to text by a speech-to-text converter 342, and theresulting text then may be processed by the customer automation system300 or customer chatbot 410 so that an appropriate response in thedialogue tree may be found. If the agent's response cannot be processedby the dialogue tree, the customer automation system 300 may ask theagent to rephrase the response or may connect the customer to the agentin order to complete the transaction.

While the customer automation system 300 is conducting the interactionwith the live agent in accordance with the interaction script, the agentmay indicate their readiness or desire to speak to the customer. For theagent, readiness might occur after reviewing all of the media documentsprovided to the agent by the customer automation system 300 and/or afterreviewing the customer's records. In exemplary embodiments, the scriptprocessing module 325 may detect a phrase spoken by the agent to triggerthe connection of the customer to the agent via the communicationchannel (e.g., by ringing the customer device 205 of the customer). Suchtriggering phrases may be converted to text by the speech-to-textconverter 342 and the natural language processing module 310 then maydetermine the meaning of the converted text (e.g., identifying keywordsand/or matching the phrase to a particular cluster of phrasescorresponding to a particular concept).

As another example, the customer automation system 300 may presentautomatically generated “quick actions” to the customer based on thecustomer's inferred intent and other data associated with the ongoinginteraction. In some circumstances, the “quick actions” require nofurther input from the customer. For example, the customer automationsystem 300 may suggest sending an automatically generated text or emailmessage to the contact center directly from a main menu screen, wherethe message describes the customer's issue. The message may be generatedautomatically by the script processing module based on a messagetemplate provided by the script, where portions of the template thatcontain customer-specific and incident-specific data are automaticallyfilled in based on data collected about the customer (e.g., from thecustomer profile) and that the customer has supplied (e.g., as part ofthe initial customer input). For example, in the case where the customerinput references a question about a possible double billing by aparticular service provider, the script processing module 325 canreference previous billing statements, which may be stored as part ofthe customer profile 330, to look for historical charges. The customerautomation system 300 infers from these previous billing statements thatthe amount charged for the period in question was unusually high. Insuch cases, the system may automatically generate a message which maycontain the information about the customer's typical bills and theproblem with the current bill. The customer can direct the customerautomation system 300 to send the automatically generated messagedirectly to the contact center associated with the service provider. Inexemplary embodiments, the script may provide multiple templates, andthe customer may select from among the templates and/or edit a messageprior to sending, in order to match the customer's personality orpreferred tone of voice.

Embodiments of the present invention include methods and systems foridentifying outstanding matters or pending actions for a customer thatneed additional attention or follow-up, where those pending actions wereraised during an interaction between the customer and a contact center.Once identified, other embodiments of the present invention includemethods and systems for automating follow-up actions on behalf of thecustomer for moving such pending actions toward a resolution. Forexample, via the automation resources disclosed herein, the personal bot405 may automate subsequent or follow-up actions on behalf of acustomer, where those follow-up actions relate to actions pending from aprevious interaction with a customer service provider. As will beappreciated, this type of automation is primarily aimed at those actionsnormally occurring within the post-contact or post-interaction stage ofa customer interaction, however it also includes the automation ofaction that also can be characterized as preceding or prompting asubsequent customer interaction.

With continued reference to FIG. 8, attention will now focus on aspectsof the present invention aimed at gathering, maintaining, analyzing, andusing customer data and profiles. For example, systems and methods aredisclosed for building highly personalized customer profiles thatfacilitate the mining and use of customer data. As will be seen, thecustomer profiles of the present invention may be used in several ways,including implementing personalized customer services aimed at improvingthe customer experience.

The present invention discloses improved systems and methods forgathering, maintaining, analyzing, and using customer data and profiles.For example, systems and methods are disclosed for building highlypersonalized customer profiles that facilitate the analysis and miningof customer data. From there, the customer profiles of the presentinvention may be used in several ways, including implementingpersonalized customer services aimed at improving the customerexperience and/or removing the interaction “friction” that normallyoccurs between customers and contact centers. On the customer-side ofthe interaction, for example, routing strategies can become morepersonalized in accordance with specific customer preferences and apresent emotional state, thereby making routing more customer centric.On the contact center-side of the interaction, the present customerprofiles also may be used toward improving contact center operations,such as, for example: making call forecasting more context oriented andreliable; improving handle time predictions and queue optimization;improving outbound campaigns (e.g., by targeting customers who are morelikely to see value in and respond positively to a particular offer);improving agent assists or automated processes with more customerpersonalization (e.g., by anticipating customer needs to reduce thesteps needed to complete an interaction and/or alleviate need forcustomer to provide information during an interaction); and improvingcustomer communications through greater personalization.

Before proceeding, several terms will first be presented and defined inaccordance with their intended usage. As used herein, “customerexperience” generally refers to the experience a customer has wheninteracting with a customer service provider and, more specifically,refers to the experience a customer has during an interaction, i.e., ashe interacts with a contact center. As used herein, “customer data”refers to any information about a customer that can be gathered andmaintained by a customer service provider. As provided below, suchcustomer data may be categorized with reference to several differentinformation types. In discussing how such data is stored, reference maybe made to a “customer profile” (such as customer profile 330), which,as used herein, refers a collection or linking of data elements relevantto a particular customer. Reference may also be made to “customerdatabases” (such as customer databases 610), which, as used herein,refers to a collection or linking of data elements relevant to orgathered from a large population of customers (or “customerpopulation”). Further, as stated, reference may be made interchangeablyto contact center or customer service provider. It should also beunderstood that, unless otherwise specifically limited, reference to acontact center includes reference to the associated organization orenterprise on behalf of which the customer services are being provided.This includes arrangements in which the associated organization orenterprise is providing the customer services through an inhouse contactcenter as well as arrangements in which a third-party contact centercontracts with the organization or enterprise for providing suchservices.

As shown in FIG. 8, an exemplary system 400 is shown that includes apersonal bot 405 running on a customer device 205, where the personalbot 405 facilitates the creation and maintenance of a personalizedcustomer profile database or module (or simply “customer profile”) 330.As shown in the example, the customer profile 330 may include elements330A local to the customer device 205 as well as remote or cloud hostedelements 330B. The system 600 may further include customer databases610, other customer profiles 620, and a predictor module 625.

For the sake of an example, a customer may have a mobile device or smartphone on which is running an application implementing local aspects ofthe personal bot 405. In setting up a customer profile 330, the personalbot 405 may serve as a means for the customer to input information. Forexample, the personal bot 405 may prompt and accept direct input ofinformation from the customer by voice or text. The customer may alsoupload files to the personal bot 405 or provide the personal bot 405with access to pre-existing databases or other files from whichinformation about the customer may be obtained.

The personal bot 405 also may gather information about the customer bymonitoring customer behavior and actions through the customer's use ofthe device 205. For example, the personal bot 405 may collect data thatrelates to other activities that the customer performs through thedevice, such as email, text, social media, internet usage, etc. Thepersonal bot 405 also may monitor and collect data from each of theinteractions the customer has with customer service providers, such as acontact center system 200, through the customer device 205. In this way,data may be collected from interactions occurring with many differentcontact centers.

In use, at the conclusion of each interaction, the personal bot 405 ofthe present invention may update the profile of the customer inaccordance with data gleamed from that interaction. Such interactiondata may include any of the types of data described herein. As discussedmore below, once the profile is updated, it will include data associatedwith that most recent interaction as well as data from other pastinteractions. This updated or current dataset then may be analyzed inrelation to one or more customer databases 610, which, as used herein,are data repositories housing customer data, such as interaction datarelating to past interactions, from a large population of othercustomers. The analysis may be performed with the predictor module 625,which may include a machine learning algorithm that is configured tofind data driven insights or predictors (or, as used herein,“interaction predictors”).

As used herein, the interaction predictors represent a behavioral factorattributable to the customer given the first interaction type. As willbe seen, the behavioral factor of the interaction predictor may includean emotional state, behavioral tendency, or preference for a particularcustomer given a type of interaction (also “interaction type”). Theinteraction predictor may be generated and applied in real time, forexample, by the predictor module 625. Alternatively, the interactionpredictors may be determined and stored in the customer profile 330 of agiven customer as a way to augment or further personalize the profile.Such stored interaction predictors then may be applied in futureinteractions involving the customer when found relevant thereto. Thepredictor module 625 may be a module within the personal bot 405 or, asillustrated, may be a separate module that communicates with thepersonal bot 405.

Thus, in general, a personal bot 405 may gather relevant information asa customer interacts with contact centers on his mobile device. Thepersonal bot 405 may gather other types of information, as describedabove, and then may aggregate that data to build a highly personalizedcustomer profile 330. As will be appreciated, when a customer profile iscreated and maintained by a contact center, it is generally limited todata pertaining to past interactions occurring between a customer and aparticular contact center. In the present invention, with the customerprofile 330 being created and maintained on the customer-side of theinteraction, the collection of data is not so limited. Instead data maybe gathered from any of the interactions involving the customer, whichwill typically result in a much richer set of data as it reflects awider spectrum of interactions.

The system of FIG. 8 may include a collection of data that is referredto other customer profiles 620. As will be appreciated, when versions ofthe personal bot 405 are used by many customers, data may be anonymouslygleaned from the many corresponding customer profiles 330 (as shownwithin the other customer profiles 620) so to create rich repositoriesof customer data. For example, such data repositories may includeinformation taken from a multitude of past interactions covering a widespectrum of both customers and customer service providers. As indicated,this data may be parsed and aggregated into the customer databases 610so to provide particular datasets that facilitate machine learning andother data driven analytics.

While the customer profiles 330 of the present invention may include anytype of customer data, exemplary embodiments may include several primarycategories of information. These categories include: biographic personaldata (or simply “personal data”); past interaction data (or simply“interaction data”); feedback data; and choice data. As will also beseen, present systems and methods may predict or infer certain behaviortraits, preferences, or tendencies about a customer through dataanalytics. Such predictions—which are introduced above as “interactionpredictors”—may also be stored within a customer profile 330 and thenutilized in subsequent interactions as a way of enhancingpersonalization and facilitating other customer centric features.Alternatively, the interaction predictors may be generatedcontemporaneously and used in relation to an incoming interaction.

It should be appreciated that, while the data stored within the customerprofile 330 may be discussed in categories, the customer profile 330 ofthe present invention may be structured to include an aggregatedcollection of information that may be analyzed as a whole. Further, itshould be understood that the data within a customer profile 330 may bestored locally on a customer device 204, remotely in the cloud, or somecombination thereof. Present systems and methods may further includefunctionality that protects a customer's data from unwanted disclosure.In general, the data stored within the profile of a customer iscontrolled by the customer, with the customer deciding what informationis to be shared during each interaction with an outside organization orenterprise. Thus, before any customer profile data is shared with anoutside entity, such as a contact center or other organization, presentsystems and methods may first seek to confirm with the customer thatsuch sharing is intended. Additional functionality may enable thepartial sharing and use of customer information in ways that maintain acustomer's anonymity.

In regard to the types of data stored within a customer profile 330, afirst category is referred to herein as personal data. This type of datamay include general information about the customer that is generic toall interactions with customer service providers, for example, name,date of birth, address, Social Security number, social media handles,etc. This type of data may also include biographical information, suchas education, profession, family, pets, hobbies, interest, etc. Thiscategory of data may also include data that is specific to particularcontact centers. For example, data related to authentication informationspecific to the different companies that the customer does businesswith, including usernames and passwords, may be included. Such personaldata may be added to a customer profile 330 when a customer isregistering with or setting up the mobile application, i.e., personalbot 405, on his mobile device. For example, a prompt by the personal bot405 may be provided that initiates input of the necessary information.When setting up the mobile application, the customer may be asked via auser interface generated on his customer device for certain information.Once gathered, the personal data of the customer may be made part of thecustomer's profile. The customer may update this information at anytime. As will be seen, aspects of the personal data may be used to findsimilarities with other customers, which may be used when makingpredictions about the customer.

The customer profile 330 of the present invention further may include acategory of information referred to herein as past or historicalinteraction data (or simply “interaction data”). As used herein, thisrefers to data pertaining to or measuring aspects of previous customerinteractions. Accordingly, such data may include a complete historicalrecord of data reflecting all past interaction between a customer andany contact center. Interaction data may include any of the types ofinformation described herein relating to interactions, including type orintent of the interaction, information associated with the dialoguebetween the agent and customer, such as a recording or transcript,information related to the agent, including agent type and othercharacteristics, information about results of the interaction, notesprovided by the customer or the agent, files shared during theinteraction, length of the interaction, call transfers or holds thattook place during the interaction, emotional state of the customer, andothers. The customer profile 330 may be updated after each newinteraction with such interaction data taken therefrom. The interactiondata may further include feedback data and choice data, which arediscussed below.

The customer profile 330 of the present invention further may includefeedback data, which, as used herein, refers to feedback received from acustomer that relates to a particular interaction with a contact center.As will be appreciated, feedback and survey responses may provide avaluable indication as to what went right or wrong in an interaction.Often such feedback is provided by customers at the end of aninteraction in response to surveys or ratings requests. In accordancewith the present invention, any type of feedback, including customersatisfaction score or ratings, provided by a customer at the conclusionof an interaction is saved within a customer profile 330 as feedbackdata. Systems and methods of the present invention may includefunctionality wherein the personal bot 405 gathers such feedback datafor storage within the customer profile 330. The personal bot 405 may dothis via passively recording such feedback when provided by the customerin response to a query initiated by an outside entity, such as a contactcenter. The personal bot 405 also may actively prompt for such feedbackat the end of an interaction and record any responses provided by thecustomer.

Another type of feedback data may include what will be referred toherein as “conclusory statement data”. Conclusionary statement data mayinclude data related to statements made by a customer as the interactionis concluding, where the meaning of the statements is extracted bynatural language processing. Conclusory statement data, thus, may beseen as a type of inferred feedback, i.e., feedback inferred fromstatements made while the interaction is concluding.

For example, the personal bot 405 may gather such conclusory statementdata by analyzing statements or comments made by the customer at theconclusion of an interaction and, where appropriate, inferring customerfeedback from the analysis of those statements. Specifically, suchconclusory statements by the customer may be extracted and analyzed vianatural language processing and, when the customer's statements areclear enough to infer feedback with sufficient confidence, the inferredfeedback may be gathered for storage within the customer profile 330 asa type of feedback or interaction data. As such statements are oftenhighly relevant as to how the customer feels at the conclusion of aninteraction, such inferences can prove useful, particularly where noother rating or survey response is provided by the customer for a giveninteraction. According to exemplary embodiments, for example, suchfeedback data may be used to assist contact centers in deciding on thelevel of service that a customer should receive in a next interaction.

The customer profile 330 of the present invention further may includechoice data, which, as used herein, refers to data that relates to aselection or choice made by the customer in selecting an agent. Morespecifically, choice data refers to automatically learned preferences ofthe customer that are based on the customer's manual selection of oneagent or type of agent over another agent or type of agent.

The data stored within the customer profile 330 of the present inventionmay further include interaction predictors. As used herein, aninteraction predictor is defined as a behavioral characteristic,preference, tendency, or other customer trait that, because ofcorrelations or patterns found to exist within a dataset of relevantcustomer information, can be inferred upon or attributed to a givencustomer. As will be seen, some interaction predictors may be used topredict broad traits, behaviors, or tendencies that are common to manyother customers, while other interaction predictors are highlycontextual and specific to particular type of interaction, such as, forexample, interactions involving a particular intent, emotional state, orcontact center. As will be appreciated, the interaction predictors ofthe present invention offer a way to add detail to a customer profile330 with assumed characteristics that then may be used to personalizeservices and facilitate interactions.

In deriving the interaction predictors, any of the systems and methodsdescribed herein may be used. In exemplary embodiments, as shown in FIG.8, the personal bot is configured to communicate with a predictor module625 that includes an artificial intelligence or machine learningalgorithm. As will be appreciated, the machine learning algorithm may beapplied to a dataset of customer information and, therefrom, learnknowledge in the form of data patterns correlating one or more inputfactors to one or more outcomes, with those correlations forming thebasis of the interaction predictors. For example, the machine learningalgorithm in the predictor module 625 may extract such patterns based onmonitored customer actions and associated outcomes. Once such knowledgeis acquired, it may be put to use in the form of the present interactionpredictors to predict outcomes when new inputs are encounters, such asthose presented in an incoming interaction.

Any one or more existing machine learning algorithms may be invoked todo such learning, including without limitation, linear regression,logistic regression, neural network, deep learning, Bayesian network,tree ensembles, and the like. For example, linear regression assumesthat there is a linear relationship between input and output variables,whereas, in the case of neural networks, the learning is done via abackward error propagation where the error is propagated from an outputlayer back to an input layer to adjust corresponding weights of inputsto the input layer.

For the sake of providing examples as to how such interaction predictorsmay be derived for a given customer, reference will now be made to anexemplary customer “Adam”. To begin the process, the machine learningalgorithm of the predictor module 625 may be configured to monitor agiven dataset. This dataset may be obtained from any of the severalsources of data described herein. For example, one or more data sourcesmay be derived from data maintained within Adam's own customer profile(i.e., customer profile 330). The machine learning algorithm may haveaccess to and monitor several of the types of data stored within Adam'scustomer profile, e.g., the personal data, interaction data, feedbackdata, and/or choice data.

For example, to gain insights on what works best for Adam duringinteractions, the machine leaning algorithm could monitor (i.e., use asa training dataset) Adam's interaction data and identify particularfactors that consistently correlate with more successful outcomes. As amore specific example, the machine learning algorithm of the predictormodule 625 may monitor the choice data within Adam's customerprofile—i.e., the agents that Adam selects when given a choice—toidentify patterns relating to the type of agents Adam prefers. Onceidentified, such a pattern could become the basis for an interactionpredictor, which the predictor module 625 would then cause to be storedwithin the Adam's customer profile. When circumstances later arise thatare relevant to the interaction predictor, the interaction predictorcould be recalled from Adam's customer profile and used to facilitatechoices as to how best to provide services to Adam. Specifically, forexample, the interaction predictor could be used to predict which agentout of those available would be most preferable to Adam, as will bediscussed more below.

In accordance with other aspects of the present invention, the machinelearning algorithm of the predictor module 625 may also monitor andderive datasets from one or more customer databases 610, which, as usedherein, refer to a collection of customer data gathered from “othercustomers”. For example, the customer databases 610 may include datagathered from a large customer population. Such customer databases 610may store any of the customer data types discussed herein and include amultitude of samples collected from a customer population. As anexample, one of the customer databases 610 may include data aggregatedfrom the personalized customer profiles of the present invention, wherethose customer profiles 330 correspond to customers within a customerpopulation (with those customer profiles 330 being represented by thosedepicted within the other customer profiles 620).

In accordance with an exemplary embodiment, the machine learningalgorithm may monitor or derive training datasets from the customerdatabases 610, such as a dataset that includes interaction data takenfrom previous interactions between customers within the customerpopulation and different contact centers. The machine learning algorithmmay then analyze the data within this database to identify patterns inwhich particular factors consistently correlate with certain outcomes.As before, such patterns or correlations may then become the basis foridentifying interaction predictors. Thus, based on similarities found toexist between Adam and the other customers within the customerpopulation, the predictor module 625 may cause one or more interactionpredictors to be applied to or used in connection with Adam.

When identified from a large database of customer information,interaction predictors may be found to be predictively relevant to thecustomer population as a whole or to a group or subpopulation definedwithin the customer population. Thus, in accordance with the presentinvention, the applicability of such interaction predictors to anyparticular customer, such as Adam, may be predicated on a degree ofsimilarity found to exist between Adam and a given subpopulation. Thus,the predictor module 625 may attribute such an interaction predictor toAdam only after determining that a sufficient degree of similarityexists between Adam and the customers within the correspondingsubpopulation or, put another way, whether Adam is determined to bemember of that subpopulation. Upon determining that a sufficient levelof similarity exists between Adam and that subpopulation, the predictormodule 625 may add the particular interaction predictor to Adam'scustomer profile, where it will remain until further machine learningmakes necessitates its modification or removal.

As a general example, a customer database 610 that stores interactiondata may include data collected from interactions between a customerpopulation and many different contact centers. A predictive correlationor other data driven insight—generally referred to herein as aninteraction predictor—is then identified via the machine learningalgorithm of the predictor module 625 by monitoring and analyzing thecustomer database 610. Through this analysis, it may further bedetermined that the identified interaction predictor is only applicableto a particular subpopulation within the customer population. Inaccordance with the present invention, the interaction predictor then isselectively applied to a particular customer if it is determined thatthe customer is a member of the given subpopulation or, at least,sufficiently similar to another customer within the given subpopulation.

Whether gleamed from the customer's own past behavior, based on the pastbehavior of other similar customers, or some combination thereof, oncedetermined, the interaction predictors may be applied to a particularcustomer (for example, saved within his customer profile 330) and thenused to make certain insights or predictions about that customer inorder to enhance aspects of customer service. As will be appreciated,the interaction predictors stored within a customer profile 330 may bedynamically updated as needed so that those currently stored reflectchanges, updates, or additions to the underlying datasets. For example,in an interaction that just concluded, customer Adam made an agentselection that significantly modifies the choice data stored in hiscustomer profile. According to exemplary embodiments, the machinelearning algorithm may continue to monitor Adam's customer profile (andchoice data included therein) and modify the interaction predictors inAdam's customer profile as needed given the modification to theunderlying dataset (i.e., the dataset as modified by his recentinteraction).

Changes to data within the customer databases 610 may also modify howinteraction predictors are applied to Adam. For example, the addition ofnew interaction data within a customer database may modify interactionpredictors that are identified therein. To the extent the modificationimpacts any of the interaction predictors found applicable to Adam,Adam's customer profile would be updated to reflect that. As anotherexample, if Adam inputs new personal information, such as a change inprofessional status or where he lives, existing similarities betweenAdam and certain groups within the customer population may be altered.As those similarities change, the interaction predictors that areattributed to Adam or used in interactions involving Adam will beupdated to reflect the changed similarities.

With the data and the interaction predictors stored in a given customerprofile 330, aspects of the present invention may be used to facilitatethe personalized delivery of customer services related to a present orincoming interaction. For example, contextual information or factorsrelated to the incoming interaction may be identified and, based onthose identified factors, predictions can be made about the customer bydetermining which of the stored interaction predictors are applicable.Alternatively, it should also be understood that such predictions aboutthe customer may be made contemporaneously with the incoming interactionvia the machine learning algorithm (or models developed therefrom)finding similarities in the contextual information around the incominginteraction and past interactions experienced by the customer and/orother similar customers within the customer databases 610. In eithercase, one or more interaction predictors applicable to the incominginteraction may be used to facilitate the delivery of services to thecustomer during the incoming interaction.

In accordance with exemplary embodiments, the relevant interactionpredictors along with any other relevant information from the customerprofile 330 may be packaged within an interaction profile and thendelivered to a contact center for use thereby. As will be seen, thecontact center may then use this package data or interaction profile tofacilitate decisions as to the nature of services that should beprovided to the customer during the incoming interaction. Embodimentswill now be discussed covering exemplary implementations as to how thisinformation may be used. For the sake of these example, reference againmay be made to customer Adam.

In accordance with a first example, systems and methods of the presentinvention may be used to predict a customer's emotional state in theincoming interaction. For example, based on the series of interactionsthat Adam has experienced, interaction predictors may be developed thatrelates such interactions to a pattern of emotional states, which may begleaned from analyzing interaction transcripts for language indicativeof particular emotional states. A customer's emotional state, forexample, may vary in accordance with a predictable pattern that relatesto factors such as: intent of the interaction; recent unsuccessfulefforts to resolve the same issue; unfavorable history with a certainenterprise; etc. By learning these patterns using the systems andmethods disclosed above, it now becomes possible to make predictions asto the emotional state that the customer is likely to exhibit in thenext incoming interaction.

For example, Adam calls Best Buy to enquire about an online order thathe placed last week for an iPhone. Best Buy, as a retailer, answerAdam's question, but tells him that the order was placed with Apple.Best Buy gives Adam with an order identification number and redirectshim to a customer service provider associated with Apple. Adam, nowconnected with Apple, is told by an agent that his order has beenfulfilled and sent to FedEx for shipment. The Apple agent furtherprovides a reference shipping number for tracking the order. With thisnew information, Adam goes to the FedEx webpage, however he finds thatthe tracking information fails to provide any information about hisorder. Adam now calls FedEx to inquire about it. After being on hold forseveral minutes, a FedEx agent finally informs Adam that FedEx has notreceived the requested order from Apple and that the tracking number hehas been provided is incorrect. Adam now instigates anotherinteraction—referred to as an incoming interaction for the sake ofdescribing functionality—with Apple. Each of these interactions are donethrough a customer device of Adam that has a personal bot 405 inaccordance with the present invention.

The personal bot 405 of the present invention may be tracking theinteractions Adam has instigated with the customer service providersassociated with Best Buy, Apple, and FedEx. Using systems and methodsdescribed herein, Adam's customer profile may be updated with each ofthese interactions as they happen and, through natural languageprocessing of transcripts and other available information relating tothe interactions, the personal bot 405 may become aware that: a) thesituation involves several interactions relating to common subjectmatter (i.e., the same problem); b) that Adam has already initiatedseveral recent interactions with different enterprises in an effort toresolve that problem; and c) Adam has so far been unsuccessful and theissue remains unresolved.

To continue the example, the predictor module 625 may have gleamedseveral interaction predictors that are relevant to this situation. Asdescribed above, these may have been determined via analyzing (e.g., byusing a machine learning algorithm) data associated with Adam's own pastbehavior and/or the behavior of a population or group of other customersthat are similar to Adam in ways found to be predictively relevant. Theapplicable interaction predictors, for example, may predict that thesituation is one that likely would induce a particular emotional statefor the customer, such as negative emotions like anger or frustration.Thus, by using information stored within the Adam's customer profile andrecognizing the number and subject matter of Adam's recent interactions,a prediction can be made as to Adam's emotional state coming into theincoming interaction that Adam just initiated with Apple. Specifically,it can be predicted that Adam will likely be angry or frustrated. Thistype of insight then can be used in several ways to tailor the serviceAdam receives once he connects with Apple. For example, as will bediscussed more below, this prediction may be used to select an agentthat is more adept at handling interactions with frustrated or angrycustomers.

Related to the above example, systems and methods of the presentinvention can also be used to facilitate a proactive engagement by acustomer service provider or contact center. That is, given theabove-described pattern of recent interactions logged within Adam'scustomer profile, the personal bot 405 of the present invention canpredict not only that Adam is angry or frustrated, but also that theissue remains unresolved and that Adam will soon be contacting Appleagain as he tries to find a resolution. With these types of predictions,the personal bot 405 can also include functionality whereby a particularenterprise (Apple in this case) is notified that Adam's issue remainsunresolved and Adam will likely be trying to contact Apple again. Thistype of information could then prompt Apple to proactively initiate thenext interaction before Adam does. As will be appreciated, this type ofproactive step by an enterprise would go long way toward repairing acustomer's negative feelings, while also facilitating a resolution to anongoing issue. Which is to say, if it can be predicted that a customer'sissue remains unresolved and the customer is likely to instigate anotherinteraction soon, it may be very favorable from a customer relationshipperspective for the enterprise to be the party that instigates that nextinteraction. With personal bot' s extensive customer data coveringmultiple enterprises and multiple intents, these predictions on upcominginteractions can be made and the given enterprises convenientlynotified.

Taking further advantage of the systems and methods disclosed herein,the personal bot 405 may be able to compute a severity rating for anincoming interaction. As used herein, a severity rating for aninteraction is a prediction as to how serious or important aninteraction is to a customer. Conventional contact centers typicallypredict a severity or importance for an incoming interaction based uponthe intent of the interaction. For example, for any incoming interactionwith an intent determined to be “stolen credit card”, a severity ratingof “high severity” (i.e., high level of importance) is allocated. Asanother example, for an incoming interaction with an intent determinedto be “forgotten password”, a severity rating of “moderate severity”(i.e., moderate level of importance) is allocated.

Similar to the process described above in relation to predictingemotional state, present systems and methods may learn to personalizeseverity ratings for particular customers based on the pattern ofinteractions stored in the customer profile 330 and interaction data forsimilar customers. As before, learned interaction predictors may applyspecifically to a particular customer, such as Adam. Along with intent,such interaction predictors may take into account other factors, suchas, for example, time of the day, type of enterprise, recentinteractions, and the emotional state of the customer. With thisinformation, the personal bot 405 can tailor severity ratings forincoming interactions for particular customers. As will be appreciated,different customers may view the same type of interaction with varyinglevels of importance. With the present invention, these varying levelsmay be determined, and service levels varied accordingly.

The systems and methods of the present invention also may be used insimilar ways to make other useful predictions related to incominginteractions, which then may enable improved customer service. Asdiscussed more below, a first of these include using the customerprofile 330 of the present invention to personalize routing decisionsfor customers.

As another example, based on the customer profile 330 (and interactionpredictors stored therewithin) as well as the intent and othercontextual factors related to the incoming interaction, the personal bot405 can make predictions regarding the likelihood of success ofupselling and/or cross-selling opportunities available to the givenenterprise or contact center. As an example, certain customers may bedetermined to be more approachable than others with upselling orcross-selling offers. As another example, a customer's emotional statecould be a factor that is found to correlate with the success ofupselling or cross-selling opportunities. Specifically, an angry orfrustrated state may negatively impact the likely success of attempts toupsell or cross-sell a customer. Indeed, it may be found that, incertain situations, the attempt to upsell or cross-sell such customeronly serves to make the customer angrier or more frustrated. It will beappreciated that contact centers could apply such insights toward makingmore productive routing decisions. For example, those incominginteractions that rate well in regard to upselling or cross-sellingopportunities could be steered to agents that perform better in thisarea.

As another example, the present systems and methods may be used topredict a preferred communication channel for an incoming interaction,with the preferred communication channel being the channel offering thebest chance for successful resolution given the customer. As before,based on the customer profile 330 (and interaction predictors storedtherewithin) as well as the intent and other contextual factors relatedto the incoming interaction, the personal bot 405 can predict apreferred communication channel for initiating an interaction with thecontact center. As another example, if a customer has reached out to hisbank about a forgotten password, the personal bot 405 could redirect theinteraction to a self-service portal which is configured to instantlyresolve this kind of interaction. In this way, the customer can avoidthe wait to be connected with agent that is unnecessary.

With reference now to FIG. 9, a method 650 is shown for personalizing adelivery of services to a customer (which, for clarity, will be referredto as a “first customer”) via a personalized customer profile. The firstcustomer may have a communication device, such as a smart phone, throughwhich interactions with several contact centers are conducted.

As an initial step 655, the method 650 includes the step of providing acustomer profile for storing data related to the first customer.

At a next step 660, the method 650 includes the step of updating thecustomer profile via performing a data collection process to collectinteraction data related to the interactions between the first customerand contact centers. The data collection process may be performedrepetitively so to update the customer profile after each successive oneof the interactions. Described in relation to an exemplary firstinteraction between the first customer and a first contact centers, thedata collection process may include the steps of: monitoring activity ona communication device of the first customer and, therefrom, detectingthe first interaction with the first contact center; identifying datarelating to the first interaction for collecting as the interactiondata; and updating the customer profile to include the interaction dataidentified from the first interaction. The contact centers involved inthe interactions from which the interaction data is collected mayinclude multiple different contact centers.

At a next step 665, the method 650 includes the step of identifying adataset for deriving an interaction predictor. The dataset may be based,at least in part, from the data stored within the customer profile. Morespecifically, the dataset may include the interaction data stored in thecustomer profile.

At a next step 670, the method 650 includes the step of deriving aninteraction predictor by applying a machine learning algorithm to thedataset to identify patterns therein correlating one or more inputfactors to one or more outcomes relevant to the first customer given aparticular type of interaction, which, for the sake of clarity, will bereferenced as a “first interaction type”. As explained more above, theinteraction predictor may be based on knowledge acquired by using amachine learning algorithm to “learn” a set of data or dataset. Theknowledge may relate to a behavioral factor attributable to the firstcustomer when encountering the first interaction type. According toexemplary embodiments, the behavioral factor of the interactionpredictor is defined as an emotional state, behavioral tendency, orpreference. Though other types of machine learning algorithms may alsobe used, exemplary embodiment include a neural network.

At a next step 675, the method 650 includes the step of augmenting thecustomer profile of the first customer by storing therein theinteraction predictor. The storage of the interaction predictor mayinclude linking the behavioral factor to the first interaction type tofacilitate real time retrieval, for example, when for use in relation toa subsequent or incoming interaction that is the same as the firstinteraction type.

At a next step 680, the method 650 includes the step of modifying, inaccordance with the behavioral factor, a manner in which services aredelivered to the customer in an incoming interaction. For example, anincoming interaction instigated by the first customer may be detected asbeing the same as the first interaction type. In response thisdetection, the derived interaction predictor may be retrieved from thecustomer profile of the first customer, and, upon being retrieved, therelevant behavioral factor can be identified. The manner in whichservices are delivered to the first customer in the incoming interactionmay be modified pursuant to the behavior factor. More specifically, onceidentified, the behavior factor may be transmitted to the contact centerinvolved in the incoming interaction. The contact center may then usethe insight provided by the behavior factor to modify the way itdelivers services to the first customer in the incoming interaction.

The method 650 may be performed in accordance with several additional oralternative steps, which provide a range of functionality. Further,significant terminology of the process may be defined so to the basicmethodology yields interaction predictors covering a range ofapplications. Examples of these alternatives will now be discussed.

In accordance with exemplary embodiments, the steps of the datacollection process may be performed by an automated assistant softwareprogram or application, which will be referred simply as “automatedassistant”. The automated assistant may operate on the communicationdevice of the first customer. In example embodiments, the automatedassistant is the personal bot described above. Further, the customerprofile may be stored in cloud-hosted databases, which are updated bythe automated assistant in accordance with the data collection process.As an example, the automated assistant may transmit the collectedinteraction data over a network to the cloud-hosted databases.

In exemplary embodiments, the behavioral factor of the interactionpredictor is an emotional state attributable to the first customer giventhe first interaction type. The emotional state may be represented by atleast one descriptor representative of either a negative emotional stateor a positive emotional state. For example, the emotional state may besimple indicate a satisfied emotional state or an unsatisfied one. Otherexamples include positive emotional states, such as happy, calm, orthankful, and negative emotional states, such as angry, frustrated,confused, sad, or impatient. The interaction data included in thedataset may include data from the interactions evidencing the negativeand positive emotional states. For example, the interaction data mayinclude feedback data related to an evaluation, survey, or satisfactionscore provided by the first customer after a termination of theinteraction. The interaction data may include conclusory statement datarelated to statements made by the first customer as the interaction isconcluding. As described earlier, this type of data may constitute aninferred type of feedback data. The meaning of such statements may beextracted by natural language processing.

When deriving the interaction predictors, the way in which thebehavioral factor and first interaction type are defined may be variedin accordance with a desired functionality. For example, continuing withthe behavioral factor being defined as an emotional state, the firstinteraction type may be defined as interactions having a particularintent. In such an embodiment, the resulting interaction predictorbecomes a customer-specific prediction relating to an emotional state ofthe first customer for an incoming interaction having the particularintent. As another example, the first interaction type may be defined asinteractions involving a particular contact center. In this type ofembodiment, the resulting interaction predictor becomes acustomer-specific prediction relating to an emotional state of the firstcustomer for an incoming interaction involving the particular contactcenter. Related to this embodiment, the process for generating theinteraction predictors may be repeated after successive iterations ofthe data collection process. This repetition may be done until thecustomer profile includes the interaction predictors predicting theemotional state of the first customer for interactions involving each ofthe contact centers that the first customer regular interacts with.

In accordance with exemplary embodiments, the characteristics attributedto the first customer via the interaction predictors may be accessed andmodified by the first customer. For example, the automated assistant maygenerate user interfaces on a display of the communication device of thefirst customer that shows the emotional state data for one or more ofthe contact centers. The display may further prompt the first customerfor input modifying the emotional state in any of the interactionpredictors stored within the customer profile. To continue the example,the automated assistant may receive input from the first customermodifying the emotional state of one of the interaction predictors. Theautomated assistant may then update the emotional state of theinteraction predictor in accordance with the input received from thefirst customer.

In another example, the emotional state of the interaction predictor maycomprise a severity rating, which as explained above, rates a level ofimportance the first customer places on the first interaction type. Withsuch embodiments, the interaction data included in the dataset mayinclude data from each interaction evidencing the level of importancethe first customer placed on it. The level of importance, for example,may be based, at least in part, on an analysis of an interactiontranscript in which usage of words indicative of a high level ofemotionality and/or a low level of emotionality is evaluated. In thiscase, if the first interaction type is defined by a particular intent,the resulting interaction predictor becomes a customer-specificprediction relating to a severity rating the first customer places on anincoming interaction having the particular intent.

Alternatively, the behavioral factor of the interaction predictor may bedefined as a behavioral tendency attributable to the first customergiven the first interaction type. For example, the behavioral tendencymay include an upselling/cross-selling opportunity rating, which rates awillingness of the first customer to consider an upselling orcross-selling offer given the first interaction type. In thisembodiment, the interaction data included in the dataset may includedata from interactions describing unsuccessful upselling orcross-selling offers, successful upsell or cross-selling offers, and/orservice or products purchased by the first customer in relation toupselling or cross-selling offers. As will be appreciated, in this case,if the first interaction type is defined by a particular intent, theresulting interaction predictor becomes a customer-specific predictionrelating to an upselling/cross-selling opportunity rating for the firstcustomer in an incoming interaction having the particular intent.

As another example, the behavioral factor of the interaction predictormay be defined as a preference, e.g., an agent preference, attributableto the first customer given the first interaction type. As describedmore below, the agent preference may include a preferred agentcharacteristic for the first customer given the first interaction type.With such embodiments, the interaction data included in the dataset mayinclude choice data, the choice data including preferred agentcharacteristics derived from selections the first customer makes in theinteractions when allowed to select an agent from among a plurality ofoffered agents. As will be appreciated, in this case, if the firstinteraction type is defined by a particular intent, the resultinginteraction predictor becomes a customer-specific prediction relating toa preferred agent characteristic for the first customer in an incominginteraction having the particular intent.

In alternative embodiments, the method may include providing one or morecustomer databases storing data relating to other customers, such asinteraction data relating to interactions occurring between such othercustomers and contact centers. In such embodiments, the derivation ofthe interaction predictor applicable to the first customer may becompleted in accordance with a different process. For example, theinteraction predictors may be generated by a process that includes thesteps of: identifying a dataset that includes the interaction datastored within the one or more customer databases; deriving the knowledgeof the interaction predictor by applying a machine learning algorithm tothe dataset to identify patterns therein correlating one or more inputfactors to one or more outcomes relevant to a type of customer given thefirst interaction type; and attributing the interaction predictor to thefirst customer based shared similarities between the first customer andthe type of customer. As explained in more detail above, the “type ofcustomer” is representative of a subgroup of the other customers, withthe members of the subgroup having one or more common characteristicsfound to be predictively relevant by the machine learning algorithm inregard to the generated interaction predictor. Further, the step ofattributing the interaction predictor to the first customer may includethe steps of: after the customer profile is updated by a completediteration of the data collection process, identifying data within thecustomer profile relevant to the one or more common characteristics; andconfirming that the one or more common characteristics are possessed bythe customer. For example, the one or more common characteristics mayrelate to one or more respective characteristics stored within thebiographical personal data of the customer profile. Further, in the sameway as described above, the manner in which the behavioral factor andfirst interaction type are defined may be varied to produce similaralternative embodiments.

As stated, aspects of the present invention may be aimed at improvingautomated systems for routing incoming interactions at a customerservice provider, such as the contact center 200. Specifically, systemswill be presented that further personalize routing decisions byleveraging aspects of the customer profile 330 disclosed above. In thisway, customer preferences can be better understood and then used tofacilitate agent routing selections. A routing engine 635 may beprovided in the contact center 200. The routing engine 635 may be alogic engine that makes routing decisions based on stored algorithms,models, rules, equations or other logic. The routing engine 635 may be ahub that receives data that relates to the incoming interaction,receives data from the contact center system 200 that relates to theincoming interaction, applies logic to the received data to calculate arouting recommendation. Data received from the contact center 200 mayinclude data regarding the skills, experience, availability, and othercharacteristics about the agents of the contact center, which may bestored within an agent database 640. Once the routing recommendation iscalculated, the routing engine 635 may then route an incominginteraction to a selected one of the agents by connecting theinteraction to a corresponding one of the agent devices 230.

Asynchronous Resolution of Customer Requests

By way of background, there is a revolution in customer care dictatedlargely by the technologies related to artificial intelligence (“AI”),including machine learning and deep learning using neural networks. Theparadigm of using those technologies for contact centers is to servehuman interactions with self-service AI applications, such as chat bots,voice bots, etc.), that are able to understand the conversational topicsor requests brought up by customers seeking services delivered bycontact centers. However, when the tasks requested are not understood byAI technologies (or when the tasks are too complex to be handled bysynthetic agents), a human agent may then be involved to assist inhandling the request.

These activities are generally handled synchronously by the contactcenter according to the following models. In a traditional approach, thecustomer initiates a request in real time to an agent, such as during anongoing conversation or chat, and the agent provides a response to thecustomer within that ongoing conversation or chat. With the advent of AIapplications, customer requests are often first steered to an AI poweredchatbot or voice bot. If it is determined that the automated process isunable to assist the customer, the interaction is escalated to a humanagent. In either case, the customer's request is handled in real time orsynchronously, as it is assumed that the customer has an immediate needthat has to be fulfilled or handled immediately or, at least, resolvedwithin the ongoing interaction or shortly thereafter.

Modern contact centers generally approach either scenario with thefollowing assumptions and schema. First, the customer has time availableat that moment to resolve the issue that prompted the interaction.Otherwise, so the reasoning goes, the customer would have used email orsome other asynchronous communication channels. Second, the contactcenter must provide that service to the customer immediately or within avery short time frame. Third, a first attempt at servicing the customermay include an automated process or bot. As discussed, these may includean IVR, which may be a bot having a relatively low IQ, or a Voice bot orChatbot, which may be a bot having a relatively high IQ. Fourth, if theautomated process is unable to provide the customer with the necessaryservice, the interaction should be passed to a human agent. Of course,automated processes or chatbots, even those including advanced AItechnologies, often fail to solve the customer's request and, thus, callcenters must anticipate that many customer interactions will reach ahuman agent before resolution is reached. In certain aspects, thisapproach of using both automated processes and human agents can producedesirable results, as often the blending of the human touch with botefficiencies yields effective results.

When human agents receive an interaction from an automated process, thehuman agent is generally context of what transpired between theautomated resource and the customer. So, for example, the human agentwould receive an interaction with a customer from an automated processalong with certain information that provides a context about the natureof the customer's inquiry and other relevant information, which may begenerally referred to herein as “context information”. As an example,context information may include information identifying the customer,customer contact information (email, phone, address, social, etc.), thebusiness application (quite often the tool required to do something withthe customer), business information (marketing automation toolsinformation, customer journey), as well as information pulled from acustomer profile (past interactions, preferences, etc.).

A problem with this overall approach is the pressure placed on humanagents when the interaction is transferred to them in this way. Inshort, the human agent is required to absorb a significant amount ofcontextual information while also smoothly continuing the ongoinginteracting with the customer in a manner that is both pleasant andeffective. For example, the human agent may have to scan the numerousdata fields being shown on their monitor for particular informationneeded to solve a complex problem while also soothing an alreadyfrustrated customer. And this represents just one customer interaction.Agents, of course, are asked to repeat such performances continuouslyover the course of a long shift. Such expectations are unrealistic and,further, a chief cause of the high agent turnover rate that is usual incontact centers.

Systems and methods will now be presented that offer improvements overthe above-described conventional approach. Such improvements begin withthe understanding that, in many instances, customers initiate acommunication with a contact center at a particular time not based onanticipating they will receive an answer or resolution immediately.Instead, a customer often initiates the communication at a particulartime because the customer has availability at that time to dedicate tothe activity. At such times, the customer could choose to contact thecontact center using an asynchronous communication channel, such asemail. However, emails generally take significant time and effort toprepare so that the customer's issue is presented with enough detail sothat the issue can be fully understood by the contact center and thenacted upon so that a resolution is achieved. Further, in conventionalsystems, even when the time is taken to properly prepare the email,there is no guarantee that the contact center will respond to acustomer's email or a way for the contact center to quickly follow-upwith the customer to gather information needed to resolve the issue.

The present application, thus, proposes an asynchronous resolutionengine that more efficiently leverages aspects of asynchronouscommunications to effectively resolve customer requests. Toward thisend, a system is provided that may include a personal bot assistant (or“personal bot”) and an asynchronous resolution facilitator. As will beseen, the invention of the present application can enable benefits inefficiency for both contact centers and customers.

In an exemplary embodiment, the automated personal bot assistant may besimilar to the personal bot already described. The personal botassistant may include functionality dedicated toward facilitating theresolution of customer requests. Functionality of the personal botassistant may include performing initial communications with thecustomer to determine the nature of the request (i.e., the intent) andcollecting data from the customer that is needed to reach a resolution.As an example, the personal bot assistant may be configured as a high IQautomated process or bot that is able to communicate with the customervia voice, text, or a combination thereof. The personal bot assistantmay be a fully automated process, for example, implemented as anapplication, widget on a webpage, or in any of the other ways describedherein. The personal bot assistant may include speech recognition,natural language processing, and intent recognition abilities. Thus, aspart of the intake process, the consumer may conversationally interactwith personal bot assistant, with the personal bot assistant providingprompts to determine the nature of the customer's request and/or collectinformation from the customer related to the request. The personal botassistant also may be used to communicate with customer when the requestis fulfilled or resolved. That is, the personal bot assistant maycommunicate to the customer the resolution or proposed resolution, i.e.,how an agent associated with a contact center proposes to resolve thecustomer request. The communication of the resolution further mayinclude actions that the customer may take to resolve the request. Thatis, such communication may include any follow-up actions that thecustomer may need to complete to make that happen. When necessary, thepersonal bot assistant also may follow-up with the customer during theprocess to collect any additional information that, along the way, isdetermined to be necessary for a resolving the customer's request.

In an exemplary embodiment, as stated, the present invention further mayinclude an asynchronous resolution facilitator. The asynchronousresolution facilitator (or simply “resolution facilitator”) may includean artificial intelligence (AI) powered analyzer and data collector. Theresolution facilitator may be configured similarly and/or providesimilar functionality as the above-described predictor module 625. Forexample, as with the predictor module 625, the resolution facilitatormay interact with the personal assistant bot, collect data regarding thecustomer and interactions involving the customer via connections withseveral data repositories, and provide analytic capabilities, includingAI, for determining and predicting aspects related to the customer andinteractions. In this way, the resolution facilitator providesfunctionality by which the data deemed necessary for resolving thecustomer request (which may be also referred to herein as “augmentingdata”) is collected and presented to an agent toward achieving aresolution. In accordance with an exemplary embodiment, the resolutionfacilitator constructs a resolution package, which is a package of datathat is used by the contact center and agent to efficiently resolve thecustomer request.

As part of this functionality, the resolution facilitator may receivethe data that was collected by the personal bot assistant during theinitial intake conversation with the customer, which is referred to asthe initial set of data. This initial set of data may include, forexample: customer identification and contact information; customer data(if a previous customer); a full transcription of the request; and anintent of the customer's request (as understood by the personal botassistant and based on the text of the conversation). The asynchronousresolution facilitator then builds (or formats instructions forbuilding) an agent interface (which may be constructed as a webpage)that is configured to visually display the data associated with thecustomer request. This agent interface or, more particularly, theinstructions for constructing the agent interface, is part of the datathat makes up the resolution package. As an example, the agent interfacemay be configured as an HTML webpage. The agent interface may beconfigured to display the following type so of data: the customerrequest; whether the customer is an existing customer; a link to thecustomer records; the customer records themselves; a summary of thecustomer records; if the customer is not an existing customer, a link tothe creation of a service record page; a plurality of recommendedbusiness processes or tools recommended for solving the customer request(which may be based on the intent that the customer request isdetermined to have); and/or a link to invoke one of the recommendedbusiness processes. Other types of data provided in the resolutionpackage and displayed on the agent interface is provided in thediscussion below.

According to exemplary embodiments, the asynchronous resolutionfacilitator may further determine and format metadata for associatedwith the agent interface. In such cases, the metadata is deemed toprovide insights for better servicing the customer. For example, themetadata may include customer preferences, agent skills required,business knowledge required, language required, etc. The resolutionfacilitator may then send the resolution package (which includes theagent interface and the associated metadata) to a capture point of thecontact center where it be routed in accordance with other work items.As will be appreciated, the metadata may then be used in routing therequest to an agent that is found to be most favorable in terms ofskills, experience, etc. to handle the customer request.

The data collected by the resolution facilitator may be determined bythe nature of or intent of the customer request. For the different typesof intents, the type of information collected may be updated via miningand making connections in datasets of previous interactions, asexplained in the materials above. Per methods and systems describedherein, this may include a machine learning algorithm, for example, deeplearning though the use of neural networks.

In an exemplary embodiment, the agent interface is provided toefficiently display the information collected by the personal assistantbot and the resolution facilitator for the benefit of a human agent whois brought in to find a resolution to the customer request. Thus, theresolution facilitator compiles a case related to the customer requestwith all the relevant details, including, for example, the workflow thatis to follow. Once this is done, the resolution package is routed to anappropriate agent. The agent receives the resolution package and theagent interface is displayed on the screen of the agent's computingdevice. Working in accordance with the agent interface and the workflowdescribed therein, the agent then efficiently resolves the request.

Thus, with reference to FIG. 10, a computer-implemented method 750 isshown for asynchronously resolving customer requests at a contact centerin accordance with the present invention. The method may include, atstep 755, providing a personal bot assistant and an asynchronousresolution facilitator. At step 760, the method may include, by thepersonal assistant bot: receiving a customer request from a customer,the customer request being received in an initial conversation betweenthe customer and the personal assistant bot via a personal devicecorresponding to the customer and producing a transcript of the initialconversation. At step 765, the method may include, by the personalassistant bot: determining an intent of the customer request based onthe transcript and determining customer information relating to thecustomer that is judged relevant to the determined intent. At step 770,the method may include, by the personal assistant bot: transmitting aninitial set of data to the asynchronous resolution facilitator, wherethe initial set of data may include the transcript of the initialconversation, the determined intent of the customer request, and thecustomer information. At step 775, the method may further include, bythe asynchronous resolution facilitator: receiving the initial set ofdata and assembling a resolution package that may include instructionsfor displaying an agent interface and metadata associated with the agentinterface. The assembling the resolution package may include:determining, based on the intent of the customer request, one or morerecommended business processes for resolving the customer request;generating the instructions for displaying the agent interface such thatthe agent interface, once displayed, visually communicates at least aportion of the initial set of data and the one or more recommendedbusiness processes; determining metadata for associating with the agentinterface, where the metadata is criteria for routing the customerrequest based on at least the determined intent; and transmitting theresolution package to a routing engine of the contact center. The methodmay further include using the routing engine to route the resolutionpackage to an agent device of a particular agent (or “selected agent”)of the contact center. The selected agent may be selected from among theavailable agents of the contact center based on criteria defined by themetadata. At step 780, the method may further include displaying theagent interface on the screen of an agent device to facilitate the agentresolving the customer request. Specifically, based on the instructionsreceived in the resolution package, the agent interface is displaying ona screen of the agent device. Subsequent to displaying the agentinterface on the agent device, the method may further include receivinginput from the agent device that indicates the selected agent deems thata resolution of the customer request has been achieved. From there, themethod may include providing, by the personal bot assistant,notification to the customer of the achieved resolution via the personaldevice of the customer.

In this way, the agent receives the customer request and, when initiatedby the agent, the agent interface is displayed on the agent's computingdevice, thereby displaying all the relevant information related to thecustomer request and facilitates efficient resolution. Specifically, theagent may work in accordance with the agent interface and workflowsdescribed therein and thereby resolves the request. As will beappreciated, this takes place asynchronously, thereby negating thehigh-pressure, multi-tasking demands associated with conventionalsystems. Once resolved, input received form the agent may indicate thatthis has been achieved and inform the personal bot assistant, with thepersonal bot assistant then informing the customer of the resolution aswell as pertinent information associated therewith.

In accordance with exemplary embodiments, the personal bot assistant maybe configured as an application running on the personal device of thecustomer. In such cases, the personal device may be a smart phone.Further, in preferred embodiments, the asynchronous resolutionfacilitator may be configured as a server-hosted application thatcommunicates with the personal device of the customer and otherdatabases via a network, such as, the internet.

In accordance with exemplary embodiments, the personal assistant bot mayinclude natural language processing. In such cases, the initialconversation may include an exchange of natural language voice ornatural language text between the personal assistant bot and thecustomer. The receiving the customer request in the initial conversationmay include the personal assistant bot providing prompts to determine acharacteristic of the customer request.

In accordance with exemplary embodiments, the method may furtherinclude: determining, based on analysis of the initial set of data bythe asynchronous resolution facilitator, augmenting data; and thencollecting the augmenting data. As used herein, the augmenting data isdefined as information deemed needed for resolving the customer requestbut found missing in the initial set of data. The agent interface may befurther configured to visually communicate the augmenting data. Thecollecting the augmenting data may include: providing, by the personalassistant bot, a prompt to the customer via the personal devicerequesting the needed information; receiving, by the personal assistantbot, input from the customer via the personal device providing theaugmenting information; and transmitting, by the personal assistant bot,the augmenting data to the asynchronous resolution facilitator. Inaccordance with other embodiments, the collecting the augmenting datamay include: based on the customer information contained in the initialset of data, determining an identity of the customer; and based on thedetermined identity of the customer, acquiring stored data associatedwith past interactions between the customer and the contact center.

In accordance with exemplary embodiments, the method further includesthe steps of abridging the transcript of the initial conversation. Indoing this, the abridgement may include only those portions of theinitial conversation deemed relevant to the determining of the intent ofthe customer request. In such cases, the portion of the initial set ofdata that is visually communicated by the agent interface may includethe abridgement of the initial conversation.

In accordance with exemplary embodiments, the agent interface mayinclude a link for invoking at least one of the one or more recommendedbusiness processes. Alternatively, the agent interface may include avisual representation of a workflow showing a plurality of ordered tasksneeded for the selected agent to initiate at least one of therecommended business processes.

In accordance with exemplary embodiments, the notification provided tothe customer of the achieved resolution may include a listing of one ormore follow-up actions that the customer needs to complete. Inaccordance with other embodiments, the notification of the resolutionmay include a link invoking at least one of the one or more follow-upactions that the customer needs to complete.

In accordance with exemplary embodiments, the criteria of the metadatamay include a plurality of agent characteristics that are judgedadvantageous for handling the customer request given the determinedintent of the customer request.

As one of skill in the art will appreciate, the many varying featuresand configurations described above in relation to the several exemplaryembodiments may be further selectively applied to form the otherpossible embodiments of the present invention. For the sake of brevityand taking into account the abilities of one of ordinary skill in theart, each of the possible iterations is not provided or discussed indetail, though all combinations and possible embodiments embraced by theseveral claims below or otherwise are intended to be part of the instantapplication. In addition, from the above description of severalexemplary embodiments of the invention, those skilled in the art willperceive improvements, changes and modifications. Such improvements,changes and modifications within the skill of the art are also intendedto be covered by the appended claims. Further, it should be apparentthat the foregoing relates only to the described embodiments of thepresent application and that numerous changes and modifications may bemade herein without departing from the spirit and scope of the presentapplication as defined by the following claims and the equivalentsthereof.

That which is claimed:
 1. A computer-implemented method for resolvingcustomer requests at a contact center, wherein the contact centercomprises a customer service organization having agents that interactwith customers to resolve the customer requests, the method comprising:providing a personal bot assistant, the personal bot assistantcomprising an application running on the personal device of the firstcustomer; providing an asynchronous resolution facilitator, theasynchronous resolution facilitator comprising a server-hostedapplication that communicates with the personal bot via a network by thepersonal assistant bot, receiving a customer request from a firstcustomer, the first customer request being received in a firstconversation between the first customer and the personal assistant botvia a personal device corresponding to the first customer; producing atranscript of the first conversation; determining an intent of thecustomer request based on the transcript; determining customerinformation relating to the first customer relevant to the determinedintent; transmitting an initial set of data to the asynchronousresolution facilitator, the initial set of data including the transcriptof the first conversation, the determined intent of the customerrequest, and the customer information; by the asynchronous resolutionfacilitator, receiving the initial set of data and assembling aresolution package that includes an agent interface and metadataassociated with the agent interface, wherein the assembling theresolution package comprises: determining, based on the intent of thecustomer request, one or more recommended business processes forresolving the customer request; generating the agent interfaceconfigured to visually display at least a portion of the initial set ofdata and the one or more recommended business processes; transmittingthe resolution package to a routing engine of the contact center; usingthe routing engine to route the resolution package to an agent device ofa selected agent of the contact center, the selected agent beingselected from among the agents of the contact center based on thecriteria of the metadata; and based on the instructions received in theresolution package, displaying the agent interface on a screen of theagent device; subsequent to displaying the agent interface on the agentdevice, receiving input from the agent device that indicates theselected agent has completed preparing a resolution for the customerrequest; and providing, by the personal bot assistant, notification tothe first customer of the resolution via the personal device of thefirst customer.
 2. The method according to claim 1, wherein theresolution package further includes metadata associated with the agentinterface; wherein the assembling the resolution package furthercomprises determining the metadata for associating with the agentinterface, wherein the metadata comprises criteria for routing, thecriteria for routing based on the intent of the customer request; andwherein the selected agent is selected from among the agents of thecontact center based on the criteria for routing contained in themetadata.
 3. The method according to claim 1, wherein the personalassistant bot comprises natural language processing and the firstconversation comprises an exchange of natural language voice or naturallanguage text; and wherein the personal device of the first customercomprises a smart phone.
 4. The method according to claim 1, furthercomprising: determining, based on analysis of the initial set of data bythe asynchronous resolution facilitator, augmenting data, the augmentingdata deemed needed for resolving the first customer request but foundmissing in the initial set of data; and collecting the augmenting data;wherein the agent interface is configured to also visually communicatethe augmenting data.
 5. The method according to claim 4, wherein thecollecting the augmenting data comprises: providing, by the personalassistant bot, a prompt to the first customer via the personal devicerequesting the needed information; receiving, by the personal assistantbot, input from the first customer via the personal device providing theaugmenting information; and transmitting, by the personal assistant bot,the augmenting data to the asynchronous resolution facilitator.
 6. Themethod according to claim 4, wherein the collecting the augmenting datacomprises: based on the customer information contained in the initialset of data, determining an identity of the first customer; and based onthe determined identity of the first customer, acquiring stored dataassociated with past interactions between the first customer and thecontact center.
 7. The method according to claim 1, further comprisingthe steps of abridging the transcript of the first conversation so thatthe abridgement includes only those portions of the first conversationdeemed relevant to the determining of the intent of the customerrequest; wherein the at least a portion of the initial set of datavisually communicated by the agent interface comprises the abridgementof the first conversation.
 8. The method according to claim 1, whereinthe agent interface comprises links for invoking at least one of the oneor more recommended business processes.
 9. The method according to claim1, wherein the agent interface comprises a visual representation of aworkflow showing a plurality of ordered tasks needed for the selectedagent to complete at least one of the one or more recommended businessprocesses.
 10. The method according to claim 1, wherein the notificationof the achieved resolution comprises a listing of one or more follow-upactions that the first customer needs to complete.
 11. The methodaccording to claim 10, wherein the notification of the resolutioncomprises a link invoking at least one of the one or more follow-upactions that the first customer needs to complete.
 12. The methodaccording to claim 1, wherein the agent interface comprises an HTMLwebpage.
 13. The method according to claim 1, wherein the criteria ofthe metadata include a plurality of agent characteristics deemedadvantageous for handling the customer request given the intent of thecustomer request.
 14. The method according to claim 1, wherein thereceiving the customer request in the first conversation comprises thepersonal assistant bot providing prompts to determine a characteristicof the customer request.
 15. A system related to customers orchestratingengagements with service providers, the system comprising: a processor;and a memory, wherein the memory stores instructions that, when executedby the processor, cause the processor to perform: providing a personalbot assistant and an asynchronous resolution facilitator; by thepersonal assistant bot, receiving a customer request from a firstcustomer, the first customer request being received in a firstconversation between the first customer and the personal assistant botvia a personal device corresponding to the first customer; producing atranscript of the first conversation; determining an intent of thecustomer request based on the transcript; determining customerinformation relating to the first customer relevant to the determinedintent; transmitting an initial set of data to the asynchronousresolution facilitator, the initial set of data including the transcriptof the first conversation, the determined intent of the customerrequest, and the customer information; by the asynchronous resolutionfacilitator, receiving the initial set of data and assembling aresolution package that includes an agent interface and metadataassociated with the agent interface, wherein the assembling theresolution package comprises: determining, based on the intent of thecustomer request, one or more recommended business processes forresolving the customer request; generating the agent interface such thatthe agent interface, when displayed, visually communicates at least aportion of the initial set of data and the one or more recommendedbusiness processes; determining the metadata for associating with theagent interface, wherein the metadata comprises criteria for routing thecustomer request based on the determined intent; transmitting theresolution package to a routing engine of the contact center; using therouting engine to route the resolution package to an agent device of aselected agent of the contact center, the selected agent being selectedfrom among the agents of the contact center based on the criteria of themetadata; and based on the instructions received in the resolutionpackage, displaying the agent interface on a screen of the agent device;subsequent to displaying the agent interface on the agent device,receiving input from the agent device that indicates the selected agentdeems a resolution of the customer request is achieved; and providing,by the personal bot assistant, notification to the first customer of theresolution via the personal device of the first customer.
 16. The systemaccording to claim 15, wherein the personal bot assistant comprises anapplication running on the personal device of the first customer, andwherein the personal device comprising a smart phone; and wherein theasynchronous resolution facilitator comprises a server-hostedapplication that communicates with the personal device of the firstcustomer via a network.
 17. The system according to claim 15, whereinthe personal assistant bot comprises natural language processing and thefirst conversation comprises an exchange of natural language voice ornatural language text.
 18. The system according to claim 15, wherein theinstructions stored by the memory, when executed by the processor,further cause the processor to perform: determining, based on analysisof the initial set of data by the asynchronous resolution facilitator,augmenting data, the augmenting data deemed needed for resolving thefirst customer request but found missing in the initial set of data; andcollecting the augmenting data; wherein the agent interface isconfigured to visually communicate the augmenting data.
 19. The systemaccording to claim 18, wherein the collecting the augmenting datacomprises: providing, by the personal assistant bot, a prompt to thefirst customer via the personal device requesting the neededinformation; receiving, by the personal assistant bot, input from thefirst customer via the personal device providing the augmentinginformation; and transmitting, by the personal assistant bot, theaugmenting data to the asynchronous resolution facilitator.
 20. Themethod according to claim 18, wherein the collecting the augmenting datacomprises: based on the customer information contained in the initialset of data, determining an identity of the first customer; and based onthe determined identity of the first customer, acquiring stored dataassociated with past interactions between the first customer and thecontact center.